IoT Inspector: Crowdsourcing Labeled Network Traffic from Smart Home Devices at Scale

09/21/2019 ∙ by Danny Yuxing Huang, et al. ∙ 0

The proliferation of smart home devices has created new opportunities for empirical research in ubiquitous computing, ranging from security and privacy to personal health. Yet, data from smart home deployments are hard to come by, and existing empirical studies of smart home devices typically involve only a small number of devices in lab settings. To contribute to data-driven smart home research, we crowdsource the largest known dataset of labeled network traffic from smart home devices from within real-world home networks. To do so, we developed and released IoT Inspector, an open-source tool that allows users to observe the traffic from smart home devices on their own home networks. Since April 2019, 4,322 users have installed IoT Inspector, allowing us to collect labeled network traffic from 44,956 smart home devices across 13 categories and 53 vendors. We demonstrate how this data enables new research into smart homes through two case studies focused on security and privacy. First, we find that many device vendors use outdated TLS versions and advertise weak ciphers. Second, we discover about 350 distinct third-party advertiser and tracking domains on smart TVs. We also highlight other research areas, such as network management and healthcare, that can take advantage of IoT Inspector's dataset. To facilitate future reproducible research in smart homes, we will release the IoT Inspector data to the public.



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1. Introduction

Internet-connected consumer devices, also known as smart home or Internet of things (IoT) devices, have seen increasingly widespread adoption in recent years. These new technologies create new challenges and research opportunities for ubiquitous computing. Conventional challenges include security (e.g., distributed denial-of-service attacks by IoT botnets (Antonakakis et al., 2017)); privacy (e.g., toys transmitting sensitive information about children to third parties (Chu et al., 2019)); and device inventory and management (e.g., determining what devices are connected to a network (Ortiz et al., 2019a)). Ultimately, data about smart home devices—and the usage of these devices—holds tremendous opportunities for understanding how people use IoT technologies and for designing new ubiquitous computing applications that rely on the collection or analysis of data from these devices.

However, this research needs large amounts of labeled data from smart home devices, which is challenging to obtain at scale for several reasons:

(i) Scaling challenges.

According to one estimate 

(Rob van der Meulen, 2017), there are more than 8 billion Internet-connected devices in the world. Many of these devices are on private home networks (Kumar et al., 2019). Yet, analysis of smart home devices often requires either physical or local network access to the devices themselves; as a result, much of the existing work operates in small-scale lab environments (Apthorpe et al., 2018; Wood et al., 2017). Researchers have occasionally deployed custom hardware in consumer homes to gather information about devices in homes (Sundaresan et al., 2014; Schmitt et al., 2018), but these types of deployments often require significant effort, since they require users to install a (sometimes costly) physical device on their home networks. Another approach is to scan the Internet for exposed devices (Antonakakis et al., 2017). However, this approach omits devices behind gateway routers that act as network address translators (NATs).

(ii) Labeling challenges. Absent a large corpus of ground-truth device labels, researchers often can infer the identities of only a limited set of devices (Feng et al., 2018). Researchers have previously published analyses of proprietary data from Internet-wide scans have been analyzed (Kumar et al., 2019), but these datasets are not public and typically do not have specific or reliable device labels.

These limitations make it difficult to carry out empirical ubiquitous computing research based on data from real smart homes, ranging from measurements of security/privacy violations in the wild (Apthorpe et al., 2018; Wood et al., 2017)

to training machine learning algorithms for modeling device behaviors 

(Mirsky et al., 2018; Miettinen et al., 2017) or inferring device identities (Ortiz et al., 2019a; Meidan et al., 2017b).

Our solution. We have constructed the largest known dataset of labeled smart home network traffic by developing and releasing an open-source application, IoT Inspector, that crowdsources the data from within home networks in a way that respects user privacy (Section 3).111 Our Institutional Review Board (IRB) has approved the study. Since we released the software on April 10, 2019, IoT Inspector has collected network traffic from 4,322 global users and 44,956 devices, 12,690 of which have user-provided labels. We have validated the correctness of these labels against external information; we discuss the challenges of label validation and our validation approach in Section 4. The data are also still growing, as users are actively downloading and using IoT Inspector at the time of writing.

This unique dataset will enable many types of research that have otherwise suffered from limited scale and labels. Similar to how ImageNet 

(Deng et al., 2009)

advanced the field of computer vision, we hope to contribute to smart home research by providing our data to expand the scope of empirical analyses and develop more generalizable or realistic models. Since we released IoT Inspector, seven research groups have contacted us to ask about using the data in a wide variety of ways, including:

  • Training machine learning models for device identification and anomaly detection.

  • Measuring the security risks of IoT messaging systems such as MQTT (MQTT, 2019).

  • Identifying third-party services in the smart home ecosystem.

  • Inferring and labeling human activities to understand the privacy risks of devices.

  • Facilitating the collection of data concerning the habits of individuals in homes (e.g., eating, sleeping, screen time) to help answer questions related to personal health.

To demonstrate the potential of the IoT Inspector dataset for ubiquitous computing research, we analyzed a subset of the data collected between April 10 and May 5, 2019 to study the pervasiveness of two security & privacy issues plaguing smart homes: incorrect encryption of network traffic and communication with third-party advertisers and trackers. During this period, 25% of IoT Inspector users collected at least 2.8 hours of traffic each, and 10% of these active users collected at least 12.4 hours of traffic each. Additionally, 1,501 users manually labeled the identities of 8,131 devices across 53 manufacturers (Section 5).

We found that although 46 out of 53 observed vendors use TLS on their devices, devices from several popular vendors use outdated TLS versions or advertise insecure ciphers (Section 6.1). We also found that 14 out of 19 observed smart TV vendors communicate third-party advertising and tracking services, identifying 350 distinct third-party advertiser and tracking domains. This is the first known identification of specific third-party services that could collect and aggregate behavioral and lifestyle data across a range of smart devices that a user might have in his or her home (Section 6.2).

In addition to these two case studies, we discuss other types of empirical ubiquitous computing research that the IoT Inspector dataset enables, ranging from security and privacy to network management and healthcare (Section 7).

This work makes the following contributions:

  • We have crowdsourced the largest known dataset of labeled, real-world, smart home network traffic and device labels using IoT Inspector, an open-source tool that we developed to help gather this data at scale..

  • Through an initial analysis of the dataset, we discovered widespread security and privacy with smart home devices, including insecure TLS implementation and pervasive use of tracking and advertising services. Such information is uniquely available in the IoT Inspector dataset. In addition to these preliminary case studies, we highlight other types of ubiquitous computing research that can use the dataset.

  • We make the dataset available to interested researchers (Section 5.3). This includes the anonymized network traffic (in the form of device identifier, timestamp, remote IP or hostname, remote port, protocol) and device labels (in the form of device identifier, category, vendor).222

2. Related Work

We first discuss existing techniques to obtain large, labeled traffic datasets and their relation to IoT Inspector (Section 2.1). We then describe previous and ongoing smart home studies that could benefit from a large-scale, labeled dataset such as the one IoT Inspector has collected (Section 2.2).

2.1. Crowdsourcing labeled traffic datasets at scale

Existing techniques to obtain labeled network traffic at scale face multiple challenges. In particular, lab studies are restricted to a small set of devices (Apthorpe et al., 2018; Wood et al., 2017), while Internet-scanning omits devices behind NATs and often produces limited device labels (Antonakakis et al., 2017; Feng et al., 2018).

Hardware-based approaches

We design IoT Inspector to crowdsource the network traffic and labels of smart home devices from a large user population, following in the footsteps of a number of previous crowdsourcing studies. For example, multiple researchers have deployed custom routers to collect the participants’ home traffic: Chetty et al. (Chetty et al., 2011) developed Kermit, a router-based tool, to help users diagnose slow network connectivity. BISmark (Sundaresan et al., 2014; Grover et al., 2013) collected network performance characteristics through deploying routers in home networks. NetMicroscope (Schmitt et al., 2018) analyzed the quality of video streaming services through custom routers in participants’ home networks. However, unlike IoT Inspector, the hardware-based approaches used in these studies are difficult to scale to more users due to the cost of hardware and shipment.

Software-based approaches

We are not aware of any software tools other than IoT Inspector that collect smart home traffic at scale. Netalyzr (Kreibich et al., 2010) was a web-based application that helped users analyze home network performance and also gathered network statistics from 99,000 different public IP addresses. DiCioccio et al. (DiCioccio et al., 2012) developed HomeNet Profiler (DiCioccio et al., 2013) to explore how effectively UPnP could be used to measure home networks. While software tools are typically easier to deploy than hardware routers, most such tools have actively probed the home network (e.g., by performing a “scan”) rather than passively collecting traffic.

IoT Inspector combines the benefits of hardware and software data collection platforms. By designing IoT Inspector as a software tool, we avoid some of the deployment barriers that router-based studies face. We also develop IoT Inspector to behave like a router and intercept network traffic via ARP spoofing (Section 3.1), thereby building a dataset of smart home network traffic at scale. Furthermore, we draw inspiration from Netalyzr (Kreibich et al., 2010) and design IoT Inspector to benefit users, with the goal of promoting participation and user engagement (Section 3.4). At the same time, we make user privacy our first-order concern (Section 3.3) much as in previous work (Sundaresan et al., 2014).

2.2. Smart home research that could benefit from IoT Inspector data

The increasing prevalence of smart home devices has spurred researchers to investigate a variety of problems using empirical methods. These studies have typically relied on either small-scale laboratory-based data, or proprietary datasets.

Discovering security and privacy violations

Past work has explored security and privacy issues of a small set of smart home devices in lab settings. Chu et al. and Sasha et al. (Chu et al., 2019; Shasha et al., 2018) found a variety of security flaws in IoT children’s toys; Wood et al. (Wood et al., 2017) found cleartext health information in home IoT medical device communications; and Acar et al. (Acar et al., 2018) presented web-based attacks on smart home devices that host local webservers, demonstrating their real-world applications on seven home IoT devices (e.g., Google Home and Chromecast). A larger dataset of labeled network traffic enable the study the problems across a much wider array of devices and vendors.

Other studies have relied on data from actively “scanning” devices on the Internet or in home networks. Antonakakis et al. (Antonakakis et al., 2017) scanned the Internet and identified public-facing devices compromised by the Mirai botnet; Kumar et al. (Kumar et al., 2019) used a proprietary dataset from an antivirus company to discover vulnerable devices within home networks. Despite the scale of these studies, researchers do not have reliable labels of device types and vendors; rather, they could only infer the device identities using a variety of signals (e.g., based on HTTP response headers (Feng et al., 2018), default passwords (Antonakakis et al., 2017), or proprietary rules (Kumar et al., 2019)). In contrast, lab studies permit knowledge of device types and vendors but are limited to much smaller scale. IoT Inspector allows the collection of a large, labeled dataset of network traffic from devices that are deployed in real networks.

Modeling device activities

Other past work has applied machine learning identify unknown devices (Ortiz et al., 2019a; Meidan et al., 2017b) and detect anomalous behaviors (Mirsky et al., 2018; Miettinen et al., 2017). These studies used a small number of devices in lab settings for training and validation. It is unclear, however, whether the models would be equally effective if tested in real-world settings, with a larger set of devices as being used by real humans.

3. Crowdsourcing Smart Home Network Traffic at Scale

In this section, we describe the design and implementation of IoT Inspector to crowdsource labeled network data at scale. We developed IoT Inspector, an open-source tool that consumers can download on their computers at home to analyze the network activities of their smart home devices. To attract users to participate in this crowdsourcing effort, we designed IoT Inspector in a way that makes setup easy; our goal was to make the application as close to “one click” as possible. Users can run IoT Inspector on macOS- and Linux-based computers333The Windows version is still under development at the time of writing. without dedicated hardware such as custom routers. Furthermore, we designed IoT Inspector to promote user engagement by showing a real-time analysis of their smart home network traffic on the user interface, which allows users to identify potential security and privacy problems. With user consent, IoT Inspector anonymizes and uploads the network data and device labels to our server, where we preprocess the data for researchers to analyze.

3.1. Designing a software tool to capture traffic

Many home network measurement platforms (Sundaresan et al., 2014; Schmitt et al., 2018) require participants to first obtain custom routers to collect network traffic. This requirement, however, limits the scale of such studies due to the cost of hardware and shipment.

To minimize the setup cost and facilitate deployment at scale, we design IoT Inspector to be a software-based data collection tool that users can install in a relatively small number of steps. First, the user needs a macOS- or Linux-based computer that is connected to the smart home network. From IoT Inspector’s website, the user can download the precompiled executable or the source code.444Users have to accept macOS’s warning that the app is not from the official AppStore. We cannot submit the app to the AppStore because it does ARP spoofing. The executable includes all necessary platform-dependent libraries, so that the user can launch IoT Inspector without needing to install additional packages. When a user runs IoT Inspector for the first time, it displays a consent form—approved by our university’s IRB—that details what data IoT Inspector collects and that IoT Inspector poses no more than minimal risk to users.

Upon the user’s consent, IoT Inspector automatically discovers devices on the network and captures traffic from select devices, as outlined below:

Figure 1. A screenshot of IoT Inspector’s user interface that shows a list of devices on the network. Using the checkboxes, users can select which devices to monitor, i.e., from which IoT Inspector can capture network traffic

Discovering devices via ARP scanning

Upon launch, IoT Inspector automatically sends out ARP packets to all IP addresses in the local subnet to discover devices. At the same time, IoT Inspector opens the user interface (UI) in a browser window that shows a list of device IP addresses and MAC addresses currently on the network. We show an example screenshot of this UI in Figure 1. From this UI, users have to explicitly indicate which of the listed devices IoT Inspector is allowed to monitor (i.e., collect traffic). To help users choose what devices to monitor, IoT Inspector also displays the likely identities of individual devices, using external data sources such as the IEEE Organizationally Unique Identifier (OUI) database (which shows the manufacturers of network chips (IEEE, )) along with mDNS and SSDP announcements that may include a device’s identity (as collected by Netdisco (Netdisco, )).

Capturing traffic via ARP spoofing

By default, IoT Inspector only ARP scans the network to discover devices. For IoT Inspector to capture any device traffic, a user would have to explicitly indicate which device(s) to monitor from the device list (Figure 1).

For each monitored device, IoT Inspector continuously sends two ARP spoofing packets every two seconds, similar to Debian’s arpspoof utility (Debian, ): one packet is sent to the monitored device using the IP address of the router as the source, and one packet is sent to the router using the IP address of the monitored device as the source. In this way, IoT Inspector can intercept all traffic between the monitored device and the router.

The ARP spoofing packets are unlikely to consume significant bandwidth, although network latency is likely to be increased due to packets taking extra hops to go through IoT Inspector. Each ARP packet is typically 42 bytes. If there are monitored devices (excluding the router) on the local network, then IoT Inspector would need to send out packets every two seconds, or bytes per second. In a home network of 50 devices (which is the upper limit for IoT Inspector by default), for instance, the bandwidth overhead would be 53.6 Kilobytes/second.

Through a combination of ARP scanning and spoofing, IoT Inspector is able to discover devices and capture their traffic in a way that requires minimal user engagement and no dedicated hardware. Using this captured traffic, we can generate a dataset for research (Section 3.2) and promoting user engagement (Section 3.4).

3.2. Collecting network traffic and device labels

IoT Inspector collects two types of data: network traffic and device labels.

Network traffic

Users choose which devices to monitor (Figure 1), such that packets to and from the monitored devices are captured by the computer that runs IoT Inspector. IoT Inspector parses the relevant fields from the captured packets using the Scapy Python library, removes sensitive information, and uploads the resulting data to the database server at five-second intervals. Specifically, IoT Inspector collects the following data:

  • SHA-256 hashes of device MAC addresses, using a secret salt555IoT Inspector does not share the secret salt with us. that IoT Inspector randomly generates upon first run.

  • Manufacturer of the device’s network chipset, based on the first 3 octets of the MAC address (i.e., OUI).

  • DNS requests and responses.

  • Remote IP addresses and ports.

  • Aggregated flow statistics, such as the number of bytes sent/received over five-second windows.

  • Data related to device identities, including SSDP/mDNS/UPnP messages, HTTP User-Agent strings, and hostnames from DHCP Request packets, that are useful for validating device identity labels entered by users (Section 4).

  • TLS Client Hello messages.

  • Timezone of the computer running IoT Inspector


Device labels

Recorded network traffic alone is typically insufficient for research, as it is often necessary to characterize network activities that are specific to certain models or types of devices.

Figure 2. A screenshot showing a user entering the identity information of a device into a dropdown textbox with auto-complete.

IoT Inspector therefore asks users to voluntarily label their devices’ identities. From IoT Inspector’s UI, users can enter the name (e.g., “Roku TV”), category (e.g., “TV”), and vendor (e.g., “Roku”) for one or more of their devices. IoT Inspector provides dropdown textboxes with auto-complete capabilities, so that users can select from a list of known labels. If, on the other hand, the desired labels are not in the dropdown lists, users can enter free text. We show an example of the device labeling interface in Figure 2.

IoT Inspector uploads the device labels along with the network traffic data to a central database hosted at our institution. We use this dataset to investigate two security and privacy issues of smart home devices within and across device categories and vendors (Section 6).

3.3. Protecting privacy of others in household

The design of IoT Inspector, along with our data collection, storage, and retention policies/practices, has been approved by our university’s IRB. We follow industry-standard security and privacy practices. For example, each instance of IoT Inspector uploads the captured network data to our central server via HTTPS, and we store this data on a secure, fully updated server hosted on our institution’s network. IoT Inspector only collects the data outlined in Section 3.2.

Nonetheless, the data collected may inadvertently contain sensitive information. As such, we designed IoT Inspector to allow a user to retroactively remove select data. For example, a device could be involved in sensitive activities, or a user may have accidentally monitored a medical device; in this case, the user can delete all data associated with this device from our server directly through IoT Inspector’s UI. Additionally, collected DHCP or SSDP messages may include a user’s identity (e.g., “Joe’s TV”); in this case, the user can have IoT Inspector remove all DHCP and/or SSDP messages from a specific device from our server.

Furthermore, IoT Inspector may pose privacy risks to other people on the same network who do not use IoT Inspector. Although ARP spoofing makes make it easy for IoT Inspector to start capturing traffic, this design could also potentially make it easy for a user to analyze sensitive activities of other people on the same network.

To increase the barrier of such malicious activities, we design IoT Inspector such that it does not upload any traffic from devices that show signs of being general-purpose computing devices, such as phones, tablets, or computers. We make this determination based on two data sources: (i) the HTTP User Agent string (which is often missing due to reduced adoption of unencrypted HTTP); and (ii) the FingerBank API (, ), a proprietary service that takes as input the first 3 octets of a device’s MAC address, along with a sample of five domains contacted, and outputs the device’s likely identity. By parsing this output and looking for specific keywords such as “phone,” “macOS”, “Android,” or “Windows,” we infer whether the device is potentially a smart home device or a general purpose computer.

It is possible that IoT Inspector may mistake an actual smart home device for a computer (e.g., due to false positives of FingerBank’s API). Users can manually correct this mistake by following the instructions on the IoT Inspector UI and entering the device’s MAC address (e.g., often printed on the device itself, or displayed on the settings menu if the device has a screen), thereby demonstrating that the user likely has physical access to the device. We admit, however, that this design merely increases the barrier for a malicious user; it does not completely prevent an advanced user from ARP-scanning the network, obtaining the MAC address of a targeted device, and bypassing this protection. At the same time, it is worth noting that a user who is advanced enough to bypass IoT Inspector’s protections is likely sophisticated to perform network traffic capture and monitoring without the help of IoT Inspector in the first place.

3.4. Keeping users engaged

Our goal is to not only make this dataset useful to researchers; it should also provide users with insight on their smart home networks. This draws from Netalyzr’s experience that providing benefits to users also boosted user count and engagement (Kreibich et al., 2011).

To this end, we set up an automated script on the server to preprocess the data collected, produce tables and charts in real time, and push these visualizations back to the front end for users to discover potential security and privacy issues.

Figure 3. A screenshot showing a table of endpoints, along with the countries and the number of bytes sent and received for each endpoint, for a Samsung Smart TV in our lab.

Preprocessing data

The data preprocessing pipeline involves two steps: (1) aggregating the collected packets into flows (i.e., same source and destination IP addresses, ports, and protocol) at 5-second intervals; and (2) identifying the remote endpoints that communicate with the monitored devices.

We identify remote endpoints because, by default, each packet collected only shows the communication between a monitored device and some remote IP address. An average user, however, may not know which real-world entity is associated with the IP address. As such, our automated script attempts to first find the hostname that corresponds to the remote IP address based on past DNS responses or the SNI field from previous TLS Client Hello messages. It is possible that one or both of DNS and SNI might be missing in the dataset; for example, a user could have started running IoT Inspector after the relevant DNS and SNI packets were sent/received and thus IoT Inspector would fail to capture these packets. In the case where we do not observe DNS or SNI data for a particular IP address, we infer the hostname based on passive DNS (Security, ) or reverse DNS (i.e., PTR records).

It is possible that the hostnames themselves may not always be indicative of the endpoint’s identity. For instance, an average user may not know that is a Facebook domain. To help users learn about the identities of endpoints, we use the webXray Domain Owner List to turn hostnames into human-readable company names (Libert, 2019). We also use the Disconnect list to label hostnames that are known to track users or serve advertisements (DisconnectMe, 2019). We further complement this information with a manually compiled database of common ports; for instance, if a device contacts an IP address with destination port 123, IoT Inspector shows the user that the remote service is likely an NTP time server. We show an example of these human-readable labels in Figure 3, where a Samsung Smart TV in our lab was communicating with Facebook and other advertising/tracking services.666In Figure 3, the question marks on some of the domains suggest that IoT Inspector did not observe DNS responses from the IP addresses (e.g., because the DNS responses were cached previously); as a result, IoT Inspector had to infer the domain name based on reverse DNS or passive DNS – hence the uncertainty.

Figure 4. A screenshot of bandwidth usage for individual endpoints on a Chromecast device.

Presenting data

After the automated script labels each remote endpoint, it generates tables and charts for the UI in real time. Each table shows the hostnames and/or companies that a device has communicated with since the user launched IoT Inspector, e.g., Figure 3. In contrast, each chart shows the bandwidth usage by connections with individual endpoints from a given device over time, e.g., Figure 4.

Our primary goal is for users to learn new insights about their devices, such as what third parties a device communicates with and when devices send and receive data. Our hope is that these insights also encourage more users to install IoT Inspector and keep running IoT Inspector to monitor more devices—effectively generating a larger dataset for research.

Category Labels Examples
appliance Smart appliances, e.g., thermostats and vacuums
tv Smart TVs, e.g. Roku TV
voice Voice assistants, e.g., Google Home
camera Security cameras, e.g., Amazon Ring
hub IoT control hubs, e.g., Samsung SmartThings hub
plug Smart plugs/switches, e.g., Belkin Wemo switch
office Office appliances, e.g., printers and scanners
storage Network-attached storage devices (NAS)
game Game consoles, e.g., Sony PlayStation
car Internet-connected cars and peripherals
computer General computing devices, e.g., PCs and phones
other All devices not labeled above
Table 1. Standardized device category labels.

4. Labeling Smart Home Devices

In the previous section, we described the design and implementation of IoT Inspector for the collection of network traffic data. Before we use the dataset for research, we first standardize the user-provided labels (Section 4.1). We then describe how we validate the correctness of the standardized labels (Section 4.2).

4.1. Standardizing category and vendor labels

The user-entered category and vendor labels have two initial problems.

First, users may assign devices to fragmented categories. As shown in Figure 2 and described in Section 3.2, users can either select from a dropdown the name, category, and vendor of a device, or enter an arbitrary string in a textbox with auto-compete. However, one user may categorized their smart TV as “TV”, while another user may categorize it as “smart TV” or “television” – apparently ignoring the auto-complete dropdown lists. As such, we manually analyze each user-entered category and standardize it as one of the labels in Table 1 (similar to a previous study (Kumar et al., 2019)).

Second, users may have assigned devices to inconsistent categories, as users tend to have different mental models of device categories. Some users, for instance, consider Google Homes as “voice assistants” while others consider them as “smart speakers.” In Table 2, we show examples of user-entered category labels, which vary substantially across users. In light of this issue, we pick the most salient feature of the device777We use our best judgement to decide the most salient feature of a device. as the main category and assign one of the labels in Table 1 – which, for Google Home, is “voice.” In comparison, we label smart TVs with voice assistant features, such as Amazon Fire TV, as “tv” instead.

Similar problems also occur with vendor labels. For example, users entered the vendor label of Nest Cameras as both “Nest” and “Google.” We standardize the label as “Google.”

User Labels # of Devices % of Google Homes
voice assistant 107 24.8%
smart speaker 79 18.3%
home assistant 41 9.5%
google home mini 34 7.9%
google home 34 7.9%
voice assistant speaker 27 6.2%
home mini 13 3.0%
smart speaker assistant 11 2.5%
home 9 2.1%
google mini 7 1.6%
Table 2. Top 10 user-entered category labels for Google Homes by the number of devices for each label. In total, our dataset contains 432 Google Homes.

4.2. Validating device labels

The category and vendor standardization process is based on the original name, category, and vendor labels as entered by users. Still, either or both labels can be incorrect. In this section, we describe a method to validate the standardized labels against external information, highlight the challenges of this method, and provide statistics about the distribution of devices across category and device labels.

Validation methods

We use six sources of information to help us validate category and vendor labels.

(1) FingerBank, a proprietary API (, ) that takes the OUI of a device, user agent string (if any), and five domains contacted by the device; it returns a device’s likely name (e.g., “Google Home” or “Generic IoT”).

(2) Netdisco, an open-source library that scans for smart home devices on the local network using SSDP, mDNS, and UPnP (Netdisco, ). The library parses any subsequent responses into JSON strings. These strings may include a device’s self-advertised name (e.g., “google_home”).

(3) Hostname from DHCP Request packets. A device being monitored by IoT Inspector may periodically renew its DHCP lease; the DHCP Request packet, if captured by IoT Inspector, may contain the hostname of the device (e.g., “chromecast”).

(4) HTTP User Agent (UA) string. IoT Inspector attempts to extract the UA from every unencrypted HTTP connection. If, for instance, the UA contains the string “tizen,” it is likely that the device is a Samsung Smart TV.

(5) OUI, extracted from the first three octets of a device’s MAC address. We translate OUIs into names of manufacturers based on the IEEE OUI database (IEEE, ). We use OUI to validate device vendor labels only and not device category labels.

(6) Domains: a random sample of five registered domains that a device has ever contacted, based on the traffic collected by IoT Inspector. If one of the domains appears to be operated by the device’s vendor (e.g., by checking the websites associated with the domains), we consider the device to be validated.

Our goal is to validate a device’s standardized category and vendor labels using each of the six methods above. However, this process is difficult to fully automate. In particular, FingerBank’s and Netdisco’s outputs, as well as the DHCP hostnames and UAs strings, have a large number of variations; it would be a significant engineering challenge to develop regular expressions to recognize these data and validate against our labels.

Furthermore, the validation process often requires expert knowledge. For instance, if a device communicates with, we can validate the device’s “Belkin” vendor label from our experience with Belkin products in our lab. However, doing such per-vendor manual inference at scale would be difficult.

Given these challenges, we randomly sample at most 50 devices from each category (except “computers” and “others”). For every device, we manually validate the category and vendor labels using each of the six methods (except for OUI and domains, which we only use to validate vendor labels). This random audit approximates the accuracy of the standardized labels without requiring manual validation of all 8,131 labeled devices.

For each validation method, we record the outcome for each device as follows:

  • No Data. The data source for a particular validation method is missing. For instance, some devices do not respond to SSDP, mDNS, or UPnP, so Netdisco would not be applicable. In another example, a user may not have run IoT Inspector long enough to capture DHCP Request packets, so using DHCP hostnames would not be applicable.

  • Validated. We successfully validated the category and vendor labels using one of the six methods – except for OUI and domains, which we only use to validate vendor labels.

  • Not Validated. The category and/or vendor labels are inconsistent with the validation information because, for instance, the user may have made a mistake when entering the data and/or the information from the validation methods is wrong. Unfortunately, we do not have a way to distinguish these two reasons, especially when the ground truth device identity is absent. As such, “Not Validated” does not necessarily mean that the user labels are wrong.

Figure 5. Percentage of the 522 sampled devices whose vendor or category labels we can manually validate using each of the six validation methods.

Results of validation

In total, we manually sample 522 devices from our dataset: 22 devices in the “car” category (because there are only 22 devices in the “car” category) and 50 devices in each of the remaining 10 categories. Figure 5 shows the percentage of devices whose category or vendor labels we have manually validated using each of the six validation methods.

One key takeaway from Figure 5 is that there are trade-offs between the availability of a validation method and its effectiveness. For example, the Netdisco method is available on fewer devices than the Domain method, but Netdisco is able to validate more devices. As shown on Figure 5, we can validate 72.2% of the sampled devices using Domains but only 45.0% of the sampled devices using Netdisco. One reason for this difference is that only 4.2% of the sampled devices do not have the domain data available, whereas 54.4% of the sampled devices did not respond to our Netdisco probes and thus lack Netdisco data. If we ignore devices that have neither domain nor Netdisco data, 75.4% of the remaining devices can be validated with domains, and 98.7% can be validated with Netdisco. These results suggest that although Netdisco data is less prevalent than domain data, Netdisco is more effective for validating device labels.

Despite their availability now, domain samples may not be the most prevalent data source for device identity validation in the near future, because domain names will likely be encrypted. In particular, DNS over HTTPS or over TLS is gaining popularity, making it difficult for an on-path observer to record the domains contacted by a device. Moreover, the SNI field – which includes the domain name – in TLS Client Hello messages may be encrypted in the future (Seth Schoen, 2019). These technological trends will likely require future device identification techniques to be less reliant on domain information.

Another observation from Figure 5 is that we cannot validate a high percentage of devices using certain methods – e.g., 53.6% of devices are “Not Validated” by FingerBank. Without any ground-truth knowledge of the device identities, we are unable to attribute this outcome to user errors or FingerBank’s errors (e.g. FingerBank is unable to distinguish Google Homes and Google Chromecast during our lab tests). Given this limitation, we do not discard any devices from the dataset just because we cannot validate their labels. We defer to future work to improve device identification (Section 7).

5. Dataset

On April 10, 2019, we announced the release of IoT Inspector with the help of social media. In particular, we first set up a website where the public would be able to download IoT Inspector’s executable or source code, along with documentation on how the software works, how it collects the data, and how we use the data. We host the website on a subdomain under our academic institution (i.e., We also published Twitter posts that linked to the website. Subsequently, we observed articles about IoT Inspector from a number of media outlets, including three US-based technology websites (i.e., Gizmodo, TechCrunch, and HackerNews) and the Canadian Broadcasting Corporation.

At the time of writing, IoT Inspector’s dataset includes 8,488 users who have scanned 152,460 devices. Some 4,322 of these users (i.e., active users, based on the definition in Section 5.1) have allowed IoT Inspector to capture network traffic from 44,956 of the devices. These numbers are still growing, as IoT Inspector is actively gathering labeled traffic data.

For this paper, we analyze a subset of the data collected between April 10 and May 5, 2019. This section describes aggregate statistics about the users and devices during the 26-day study period.

5.1. User statistics

All users

Every time IoT Inspector is installed, it generates and writes to disk a random User ID that persists across computer reboots. During this 26-day study period, IoT Inspector collected 6,069 such unique IDs, which we assume is the number of IoT Inspector users.

Figure 6. Number of daily active users. All dates are based on UTC.

Active users

However, not every instance of IoT Inspector was able to upload network traffic to our data collection server. In fact, only 3,388 users (55.8% of all users) uploaded some network traffic to our server; we consider these users as active users. We fail to observe any network traffic from the non-active users because some home routers may have dropped ARP-spoofing traffic, our data collection server may have been temporarily out of service due to high load,888IoT Inspector, which runs on users’ computers, stops collecting traffic if it is unable to contact the remote data collection server. or the users may not have consented to data collection.

We observe a steady number of active users every day. During the 26-day period, there were 197.9 active daily users on average, or 174.0 users in the median case. We show the distribution of the number of active users in Figure 6. We note that the number of active users tended to be higher around weekends (e.g., Saturdays April 13, 20, and 27).

Many of these active users ran IoT Inspector for a limited duration. Half of the active users collected at least 35.3 minutes of network traffic, 25% of the active users at least 2.8 hours of traffic, and 10% of the active users at least 12.4 hours of traffic.

These users are likely from around the world. Even though IoT Inspector does not collect users’ public IP addresses, we can still infer their geographical distribution based on each user’s timezone. In particular, the timezones for 64.1% of the users are between UTC -07:00 and -04:00 (e.g., between San Francisco and New York), and for 28.0% of the users the timezones are between UTC 00:00 and 03:00 (between London and Moscow).

5.2. Device statistics

All devices

Upon launch, IoT Inspector scans the local network and presents the user with a list of devices on the network. From April 10 to May 5, 2019, IoT Inspector discovered 113,586 devices in total – 8/15/26 devices per user in the 25th/50th/75th percentile.999For performance reasons, IoT Inspector discovers at most 50 devices per user. For each of these devices, IoT Inspector only collects the OUI and mDNS (if available) data.

Devices from which IoT Inspector collected traffic

By default, IoT Inspector does not collect network traffic from any device unless the user explicitly chooses to monitor it (Figure 1). As such, IoT Inspector only collected network traffic from 35,961 (31.7%) of the discovered devices during this 26-day analysis period (i.e., across the 3,388 active users).

Devices with traffic and labels

For the majority of the monitored devices, we do not have any user-entered category and vendor labels. In total, only 8,131 devices (7.2% of all devices, 22.6% of all monitored devices) have such labels, as entered by 1,501 users (24.7% of all users).

For the rest of the paper, we will only examine the network traffic data from these 8,131 devices, as their human-entered labels help us characterize security and privacy issues for particular device categories and vendors. Even with just these labeled devices, we are still looking at the largest known dataset of traffic and device labels of smart home networks in the wild.101010To our knowledge, the previously largest labeled dataset of smart home traffic from lab settings consists of 50 devices over a 13-day period collected by Alrawi et al. (Alrawi et al., 2019).

Cat # of Devices # of Vendors Most Common Vendors
appliance 1,088 25 google (25.3%), ecobee (9.5%)
tv 984 19 google (26.3%), roku (15.2%)
voice 883 2 amazon (51.1%), google (48.9%)
camera 754 18 wyze (18.2%), amazon (16.7%)
media 614 22 sonos (50.5%), denon (4.6%)
hub 567 12 philips (45.3%), logitech (18.0%)
plug 553 12 belkin (40.7%), tp-link (16.6%)
office 185 5 hp (56.8%), epson (13.0%)
storage 185 8 synology (51.4%), microsoft (13.0%)
game 161 3 nintendo (41.0%), sony (39.8%)
car 22 3 tesla (68.2%), emotorwerks (9.1%)
computer 1,292 19 apple (50.7%), raspberry (12.0%)
other 843 34 ubiquiti (6.5%), eero (3.9%)
Table 3. Overview of devices in our dataset. For each device category, we show the number of devices, number of distinct vendors, and the two vendors associated with the highest number of devices in each category.
Vendors # of Devices # of Vendors Most Common Categories
google 1,066 5 voice (40.5%), appliance (25.8%)
amazon 733 7 voice (61.5%), tv (19.2%)
sonos 310 1 media (100.0%)
philips 265 3 hub (97.0%), tv (2.3%)
belkin 226 2 plug (99.6%), appliance (0.4%)
samsung 204 6 tv (58.3%), hub (27.9%)
roku 152 2 tv (98.7%), media (1.3%)
sony 146 3 game (43.8%), tv (38.4%)
wyze 137 1 camera (100.0%)
xiaomi 130 6 appliance (72.3%), camera (10.8%)
Table 4. Vendors with the most number of devices. For each device vendor, we show the number of devices, number of distinct categories, and the two categories associated with the highest number of devices in each vendor.

Distribution of devices across labels

For these 8,131 devices, we standardize the labels (Section 4.1) and count the number of devices in each category and with each vendor. Our dataset includes a diverse set of device types and vendors, as illustrated in Tables 3 and 4. In total, there are 13 distinct categories and 53 unique vendors. Both tables show a diverse set of device categories and vendors in our data. Across our users, smart appliances, TVs, and voice assistants are the top three categories with the most devices. Across vendors, Google and Amazon have the most devices. Our dataset also includes vendors with a relatively small number of devices, such as eMotorWerks, which manufacturers smart chargers for electric cars, and Denon, which makes media players.

5.3. Data release

Interested researchers can contact us to get access to the IoT Inspector dataset as the following CSV files:

  • Device_labels.csv. Columns: device identifier, category, and vendor

  • Network_flows.csv. Columns: device identifier, timestamp of first packet, remote IP/hostname, remote port, protocol (i.e., TCP or UDP), number of bytes sent in a five-second window, and number of bytes received in the window.

  • TLS_client_hello.csv. Columns: device identifier, timestamp, TLS version, cipher suites, and TLS fingerprint (Section 6.1).

6. New Possibilities with Labeled Traffic Data at Scale

IoT Inspector’s device traffic and labels dataset – the largest of its kind that we are aware of – can create new research opportunities in diverse areas. In this section, we show two examples of questions that we can answer using our data: whether smart devices encrypt their traffic using up-to-date TLS implementations; and whether they communicate with third-party advertisement services and trackers.

6.1. Which devices encrypt their traffic?

Major browsers such as Google Chrome encourage websites to adopt HTTPS by labelling plain HTTP websites as “insecure” (Emily Schechter, 2018). However, such an effort to push for encrypted communication is yet to be seen across smart home device vendors. Our goal is to understand if TLS, a common approach to encrypting network traffic, is deployed on smart home devices and whether the encryption follows secure practices, such as using the latest TLS version and not advertising weak ciphers.

To this end, we analyze TLS ClientHello messages in the dataset. Even though the literature abounds with large TLS measurements of websites (Durumeric et al., 2017) and mobile apps (Razaghpanah et al., 2017), much of the existing work on smart home TLS is restricted to a small number of devices in lab settings (Alrawi et al., 2019). By using the IoT Inspector dataset, we provide the first and largest analysis on TLS usage for smart home devices in the wild.

Which devices use TLS? We first compare the number of devices that encrypt their traffic with respect to those that do not encrypt their traffic. Specifically, we count the number of devices from which we can extract TLS Client Hello messages, which mark the beginning of a TLS connection (regardless of the port number). We compare this number with the number of devices that communicate with port 80 on the remote host, which is likely unencrypted HTTP traffic. This comparison serves as a proxy for which devices — and also which vendors — likely send unencrypted vs encrypted traffic.

As shown in Figure 7, more devices and vendors use TLS than unencrypted HTTP. In particular, the left-hand chart shows the number of devices, thereby taking into account the purchase behaviors of our users. The right-hand chart shows the number of vendors. In total, 3,454 devices send encrypted traffic over TLS, as opposed to 2,159 devices that communicate over port 80 (presumably over unencrypted HTTP). Likewise, devices from 46 vendors use TLS, whereas devices from 44 vendors use port 80. Note that the traffic of a device can be over port 80 only, over TLS only, or both.

It is possible that we do not observe TLS traffic on certain devices. For instance, the Geeni Lightbulbs in our dataset connect with remote hosts on ports 80, 53 (DNS), and 1883 (MQTT), a lightweight messaging protocol. Despite the absence of TLS traffic, we do not know if the MQTT traffic is encrypted (e.g., over STARTTLS), because IoT Inspector does not collect the payload.

On the other hand, we observe four vendors that communicate over TLS but never connect to remote port 80, including Chamberlain (which makes garage door openers), DropCam (which makes surveillance cameras), Linksys, and Oculus (which makes virtual-reality game consoles). Absent packet payload, we do not know if these devices sent unencrypted traffic on other ports.

Figure 7. Percentage of devices and vendors that communicate with Internet hosts via port 80 (presumably over unencrypted HTTP) or TLS. Each number in parentheses counts the number of devices (left) or vendors (right) in the respective categories. Note that a device can send either or both of encrypted and encrypted traffic.

Which devices use outdated TLS versions? Even if a device communicates over TLS, the TLS implementation may not follow best practices. A smart home vendor may use an outdated or non-standard TLS library, or a vendor could (inadvertently) configure the library with insecure settings.

We focus on two examples of insecure practices that past researchers have exploited and that can potentially lead to vulnerabilities: outdated TLS versions and weak ciphers (IETF, 2015; Chromium Bugs, 2014).

We first investigate outdated TLS versions. While the latest TLS version is 1.3, the industry-accepted version is 1.2. Versions below 1.2 are known to have vulnerabilities. For instance, TLS 1.0 is subject to the BEAST attack (Chromium Bugs, 2014). Although we are not aware of any actual BEAST attacks on smart home devices, we argue that any outdated TLS versions are potential targets for attacks in the future.

# of SSL TLS Weak Ciphers
Vendor Devices 3.0 1.0 1.1 1.2 Null RC4 Anon.
vendor 1 669 0 31 0 645 0 9 15
vendor 2 614 5 67 1 613 1 397 1
sonos 186 0 0 0 186 0 0 0
vendor 4 148 1 41 0 129 0 80 1
vendor 5 135 0 9 0 135 0 130 0
vendor 6 96 3 29 8 91 0 35 27
vendor 7 69 0 0 0 69 0 34 2
wyze 53 0 0 0 53 0 0 0
vendor 9 51 0 0 4 47 0 1 0
vendor 10 46 0 5 0 41 0 5 0
Table 5. The number of devices by each vendor that use a particular TLS version and which propose a specific weak cipher in the Client Hello messages. Here, we are showing ten vendors with the most number of devices. # of Devices shows the number of devices that send out Client Hellos in SSL 3.0 or TLS 1.0-1.2. A device may be associated with multiple TLS versions and/or weak ciphers.

To understand the distribution of TLS versions across devices in our dataset, we analyze the Client Hello versions. Table 5 shows ten vendors with the most observed devices that use TLS.111111We are in the process of reporting the vulnerabilities to the respective vendors. For each vendor, we count the number of devices that use a particular TLS version. Note that a device may communicate using multiple TLS versions. For instance, Vendor 2’s TVs, as confirmed in our lab, communicate with Vendor 2’s servers using both TLS 1.0 and 1.2. Some vendors, such as Vendor 7 (which makes network-attached storage devices) and Wyze (which makes cameras), only use TLS 1.2. In total, out of these top 10 vendors, we observe 7 vendors whose devices use TLS versions below 1.2..

Which devices advertise weak ciphers? Another potentially insecure practice is advertising weak ciphers in Client Hello messages, possibly due to insecure TLS libraries or vendors’ insecure settings. We look for the use of four weak ciphers, similar to those discussed in previous works (Razaghpanah et al., 2017): (1) null ciphers, which provide no encryption and, if used, may be vulnerable to man-in-the-middle attacks; (2) anonymous ciphers (denoted as “Anon.” in Table 5), which do not offer server authentication if used; (3) export-grade ciphers, which use 40-bit keys (or shorter) to comply with old US export regulations; and (4) RC ciphers, vulnerable to several known attacks and which many vendors have since stopped supporting (IETF, 2015).

We count the number of devices whose Client Hello messages advertise these weak ciphers (Table 5). No devices in our dataset advertise export-grade ciphers. RC4 is the most frequently advertised weak cipher. In particular, 397 of Vendor 2’s devices advertise RC4 ciphers in Client Hellos; 283 of these devices are in the “voice” category, and 89 in the “TV” category. Out of the top 10 vendors in our dataset, only Sonos and Wyze do not advertise weak ciphers. These are also the only two vendors that do not use outdated TLS versions.

Despite the advertisement of weak ciphers in Client Hellos, none of the devices in our dataset actually communicated over a weak cipher after the TLS handshake is complete, presumably because the server is able to negotiate the use of a secure cipher. Even so, servers can be subject to downgrade attacks, and the advertisement of weak ciphers creates potential opportunities for exploits.


Despite many calls for smart home devices to use industry standard best-practices for data encryption, our results indicate that many vendors still use vanilla HTTP or insecure SSL/TLS versions/ciphers. This is particularly discouraging, as proper use of TLS is a low bar for smart home device vendors.

One way to mitigate this problem is through device updates. Some of the devices using deprecated versions of SSL/TLS were likely released when these versions were current. However, if devices do not support remote updates, vendors are unable to issue patches when new TLS versions are released. On the other hand, even if devices do support remote updates, the new firmware would be transmitted over a potentially vulnerable communication channel. We echo earlier recommendations (Broadband Internet Technical Advisory Group, 2016) for vendors to support smart home devices after their initial deployment and to release firmware updates to address known cryptographic vulnerabilities.

6.2. What trackers do devices communicate with?

Whereas Section 6.1 looks at the security of smart home devices, this section focuses on privacy. Our goal is to understand with what third-party services smart home devices communicate, including services that serve advertisements and track user behaviors. Although there is much existing work on such third parties on the web (Englehardt and Narayanan, 2016) and mobile devices (Razaghpanah et al., 2018), we are the first to study this problem in the smart home ecosystem at scale.

Which advertisers and trackers do smart TVs communicate with? The number of smart TVs, including televisions and streaming sticks, has increased over the past few years. An estimated 65.3% of United States (US) Internet users – nearly 182.6 million people – will have such devices in 2018 (Enberg, 2018).

Smart TVs have raised several privacy concerns, because they have access to a variety of sensitive data sources including users’ viewing histories, input from built-in microphones, and user account information, which they make available to third-party developers who build applications for these devices. Many of these devices also enable behavioral advertising. For some manufacturers, such as Roku, advertising has become the primary revenue stream (as opposed to device sales (Niu, 2018; Levy, 2018; Shields, 2018)). In fact, Vizio — a smart TV manufacturer — was recently fined by the Federal Trade Commission (FTC) for collecting users’ channel viewing histories for advertising and targeting without their consent (Releases, 2017).

Our goal is to understand which ads/tracking services smart TVs communicate with. We show that some of these services, while common on smart TVs, are less common on the web, thus highlighting the difference in the TV/web tracking ecosystem.

To identify ads/tracking domains, we check against the Disconnect list, which Firefox uses for its private browsing mode (DisconnectMe, 2019). The list includes registered domains (i.e., the domain name plus the top-level domain) that are known to be trackers and advertisers. For each domain, we count the number of devices in our dataset in the “TV” category that communicated with the domain. We also count the number of devices in the “computer” category that communicated with each of the Disconnect domains. This approach allows us to compare the relative popularity of each domain across the TV and web ecosystems.

Tracking Domains % of TVs % of Cmpters Ranking in Cmpters 47.1% 49.1% 22.6% 24.7% 18.0% 48.3% 14.9% 24.5% 10.9% 1.2% 10.9% 0.0% 10.6% 0.0% 10.6% 37.1% 7.1% 14.4% 7.1% 18.1% 6.9% 2.6% 6.3% 9.7% 5.1% 3.4% 4.3% 0.0% 4.3% 2.8%
Table 6. The percentage of TVs and computers communicating with ads/tracking domains. We show 15 domains (out of 350) that appear on the most number of TVs which are sorted by the “% of TVs” column. We also show the ranking of these domains on computers, indicated by the number of black squares. Ten squares, for instance, indicates that the ranking is in the top 10%, while one square shows that ranking is in the bottom 10%.

Out of the 984 TVs across 19 vendors, 404 devices across 14 vendors communicated with ads and trackers as labeled by Disconnect.121212The remaining 5 vendors cover only 8 devices. It is possible that we did not observe any communications with trackers and advertisers because of the small sample size. Table 6 shows the 15 ads/tracking domains that communicate with the most TVs in our dataset. We also show the percentages of TVs and computers that communicate with each of these domains. These 15 domains represent the top 4.3% of the 350 total ad/tracking domains we have observed communicating with TVs.

We compare the ranking of ads/tracking domains across TVs and computers. Google’s DoubleClick and GoogleSyndication are the top advertisers/trackers for both TVs and computers. In contrast, several ads/tracking domains are more common on TVs than the web. For instance, is a video advertising firm owned by Comcast. While it is ranked in the top 4.3% of TV’s ad/tracking list, its ranking, based on the number of computers who have contacted each domains, is between 10–20% on the web.

There are also advertising and tracking domains specific to smart TVs. For instance, three Samsung domains are in the least common 10% of observed computer ad/trackers but are prevalent for observed smart TVs. Based on the website of these domains, we speculate that Samsung TVs contact them to transmit pixel information on the smart TV screen (i.e., for automatic content recognition), gather data on the users’ viewing habits, and/or to serve advertisements (Samsung, 2019). Other vendor-specific tracking and advertising domains include, which appears on 49.2% of the Amazon TVs in our dataset, and, which appears in 38.7% of the LG TVs in our dataset.

What other trackers do smart home devices communicate with? So far, we identify advertising or tracking domains based on the Disconnect list (DisconnectMe, 2019), which is specifically used to block such domains on the web and on smart TVs. In the non-web and non-TV domain, however, we are not aware of any blacklists that target advertisers and trackers.

To this end, we look for third-party services that could potentially aggregate data across different types of devices. One example of such services is device control platforms. Device control platforms coordinate device control via mobile apps, collecting device status updates and allowing users control their smart home devices through their phones. Whereas some device vendors use first-party device control platforms, e.g., Samsung Camera uses its own XMPP server on, other vendors may choose to use third-party platforms, such as TuYa (an MQTT (MQTT, 2019) platform based in China) or PubNub (based in California).

These platforms may be able to observe changes in device state and infer the users’ behaviors and lifestyles. For example, merely keeping track of when a user turns on/off a smart plug may reveal sensitive information on a user’s life habits (e.g., when they are asleep vs awake, or whether a user is at home) (Apthorpe et al., 2019). Although we do not have evidence whether such platforms keep and/or analyze this data, by being the first to study these platforms, we are hoping to raise the awareness of the potential privacy concerns.

To identify these platforms in our dataset, we list all domains that accept connections from devices on known device-control ports, such as MQTT (1883 and 8883) and XMPP (5222 and 5223). We also look for domains that communicate with the highest number of device categories and vendors, making sure to ignore common domains such as Google, Amazon, and NTPPool (used for time synchronization).

Device Categories Evrythng PubNub TuYa Xively
appliance (25) 1 2 3 1
camera (18) 0 4 3 0
hub (12) 0 3 0 2
plug (12) 2 1 4 0
storage (8) 1 0 0 0
tv (19) 0 2 1 0
voice (2) 0 0 2 0
Table 7. Number of vendors in each category whose devices communicated with given third-party device control platforms. Each number in parentheses counts the number of vendors in the respective categories.

We identify four device control platforms, as shown in Table 7. TuYa, in particular, is used by 3 vendors in the “appliance” category (e.g., Chamberlain, which makes garage door openers) and 4 vendors in the “plug” category (e.g., Belkin and Teckin). Note that we have also observed these domains on general computing devices in our dataset, presumably contacted when smart-phone apps tried to interact with the smart home devices through the cloud.


Users or network operators may wish to block smart home devices from communicating with certain domains for privacy reasons (Adrianne Jeffries, 2018). Off-the-shelf tools such as Pi-hole (Pi-hole, 2019), are available to prevent Internet advertisements from all devices in a home network.

However, these tools relying on domain blocking will not be universally effective, as some devices observed in the IoT Inspector dataset use hardcoded DNS resolvers. In particular, out of the 244 distinct fully-qualified hostnames contacted by all Google Home devices in our dataset, 243 of them were resolved using Google’s resolver, rather than the resolver assigned by the DHCP. The Netflix app on smart TVs is another example. Out of the 75 fully-qualified Netflix-related hostnames (containing the strings “netflix” or “nflx”) contacted by smart TVs, 65 of them are resolved using, rather than the DHCP-assigned resolvers. Vendors of these TVs include Amazon, LG, Roku, and Samsung. We have analyzed Roku and Samsung TVs in the lab and are not aware of any ways for a Roku or Samsung TV user to customize DNS resolver settings on the TV. This indicates that the DNS resolver used by the Netflix app is hard coded. The use of hard coded DNS resolvers by smart home devices means that users and network operators would need to apply more sophisticated blocking tools to prevent devices communications with specific parties.

Another reason that domain blocking may not be effective is that functionalities of devices may be disrupted when certain advertising domains are blocked. On the web, blocking ads and trackers does not typically prevent the full webpage from being rendered (with the exception of anti-ad-blocking popups). On smart TVs, in contrast, we have shown in our lab that blocking advertising or tracking domains would prevent certain TV channels (i.e., apps for smart TVs) from loading. Furthermore, if a user is to block device control platforms such as TuYa or PubNub, the user would be unable to interact with their devices from their smart-phones if the smart-phones are not on the local network.

7. Future Work

IoT Inspector offers an unprecedented look into the network behavior of smart home devices in the wild. Although certain design decisions limit the scope of data that IoT Inspector can collect, the IoT Inspector dataset enables a variety of follow-up research beyond just the two examples in Section 6. This section presents these limitations and opportunities for future work using the IoT Inspector dataset and other smart home device analysis methods.

7.1. Improving IoT Inspector

We describe some of IoT Inspector’s limitations and discuss potential ways of improvement.

Promoting user engagement

The fact that 6,069 users downloaded IoT Inspector within the first 26 days of release demonstrates widespread interest and concern about smart home security and privacy. It also shows that many users, even those with security/privacy concerns, trust academic institutions enough to deploy research software in their homes. However, it is difficult to have users run IoT Inspector over an extended period of time; the median duration of traffic collected from the monitored devices is only 35.3 minutes (Section 5).

To improve user engagement, we plan to explore alternative UI designs. Currently, we based the design of IoT Inspector on our experience with existing work on home network measurement (Section 2), as well as several iterations with informal focus groups at our own university. Future work could involve a more in-depth design exercise for IoT Inspector’s interface and functionality, such as conducting qualitative user studies, or instrumenting the UI to empirically understand how existing users interact with IoT Inspector.

Collecting more data

User privacy matters. Without limitations on data collection, many users would be unlikely to employ research tools like IoT Inspector. We chose not to collect network traffic payloads for privacy reasons, but this limits the extent of possible analyses. For example, researchers and consumer advocates would like to audit whether specific devices are collecting sensitive or other personally identifiable information and transmitting it to third parties. Such behavior might be in violation of privacy policies, regulation (e.g., COPPA and GDPR), or simply against the preferences of privacy-conscious users.

We also chose not to have IoT Inspector implement more active tests, such as checking whether devices verify server certificates, because these could break TLS connections, placing user data at risk or otherwise disrupting user experience. However, such tests are necessary to determine whether the devices are following security best practices.

Both of these cases represent tradeoffs between the immediate security and privacy of individual users deploying IoT Inspector and legitimate research interests. We chose the current implementation of IoT Inspector to prioritize the privacy of individual users given our intention to publish the resulting dataset and received approval from our university’s IRB.

With these trade-offs in mind, future studies with targeted research questions or more restrictive data distribution plans could choose to collect more data than the current version of IoT Inspector, provided that the users are fully informed and they express explicit consent.

7.2. Opportunities in other research areas

While Section 6 shows examples of security and privacy research, there are other research areas that could benefit from IoT Inspector’s large-scale traffic and label dataset.

Device identification

Before analyzing the data, we manually standardized and validated device categories and vendors using six validation methods (Section 4). Although this process produced a sanitized dataset that allowed us to understand device behaviors across categories and vendors, such a practice would unlikely scale if we had collected traffic data from more devices.

We plan to explore automatic device identification in future work. Existing literature on device identification has used a variety of machine learning methods with features from network traffic rates and packet headers (Meidan et al., 2017a; Ortiz et al., 2019b; Miettinen et al., 2017) as well as acquisitional rule-based techniques (Feng et al., 2018). We plan to extend these studies, train machine learning models on IoT Inspector’s dataset, and develop new methods that would automatically infer device identities in the wild.

Anomaly detection

The ability to quickly detect misbehaving devices is an important step toward reducing threats posed by insecure smart products. Research into anomaly and intrusion detection techniques for IoT devices (Zarpelão et al., 2017; Mirsky et al., 2018) would benefit from a large training dataset of smart home device traffic from real home networks.

Although IoT Inspector includes network traffic from a diverse set of devices, the current labels only indicate device identities, rather than anomalies. There were cases where users directly emailed us about anomalous behaviors that they observed – such as two D-Link cameras that, by default, opened port 80 on the gateway and exposed themselves to the Internet – which we were able to reproduce independently in the lab. Beyond such anecdotes, however, we do not know whether the traffic in the IoT Inspector dataset was anomalous or if any devices were compromised.

We plan to expand our user labels from simply identifying devices to identifying user activities on a particular device. We could train existing anomaly and intrusion detectors on such activity labels, so that IoT Inspector could potentially recognize traffic due to user behaviors or due to malicious activities. This insight would help us better understand the prevalence of compromised or malfunctioning devices in the wild.

Health monitoring

Many smart home devices are designed to monitor human medical or psychological conditions, such as sleep monitors (Ravichandran et al., 2017) or smart watches that measure the heart rate (Maritsch et al., 2019). While such hardware devices could help researchers or healthcare providers learn about their participants’ or patients’ conditions, the cost of purchasing and deploying the hardware could potentially limit the scale of such studies.

One solution is to use the home network data to augment the data collection from existing health-monitoring devices. Apthorpe et al. (Apthorpe et al., 2019) have shown that the traffic of some smart home devices is correlated with human activities, such as whether users are home or away or whether they are asleep. Using this insight, we are exploring a new approach of health monitoring using IoT Inspector, where participants would install IoT Inspector on their home network, notify us of their User ID (i.e., effectively de-anonymizing their network data with their explicit consent), and label their activities (e.g., sleeping, eating, and watching TV). In this way, we could potentially build a large-scale dataset of network traffic with not only labels of device identities but also user activities. Based on the user activities, we could potentially infer the physical and mental state of users (e.g., whether the user is suffering sleep deprivation, or when the user spends a long time on their phones or TVs). Using this labeled data, we could train machine learning models to help researchers and healthcare providers monitor activities and health conditions of consented subjects at scale without dedicated hardware.

8. Conclusion

In response to the proliferation of smart home devices and the corresponding lack of data enabling ubiquitous computing research in this area, we crowdsourced a dataset of smart home network traffic and device labels from 44,956 devices across 4,322 users with IoT Inspector, an open-source software tool that we designed to enable large-scale, unobtrusive data collection from within smart home networks. To our knowledge, this dataset is the largest (and perhaps only) of its kind. To demonstrate the potential of this dataset to shed new insights into smart homes, we used the data to study questions related to smart home security and privacy. In particular, the IoT Inspector dataset enabled us to discover the transmission of unencrypted traffic by 2,159 smart home devices across 44 vendors and to identify insecure encryption practices; we also identified third-party trackers and data aggregators on smart TVs and a large variety of other smart devices. These insights are the tip of the iceberg in what this large—and growing—dataset can offer for ubiquitous computing research across a wide range of areas from security and privacy to human behavior.


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