Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks

09/12/2023
by   Mnassar Alyami, et al.
0

Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client-server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98 the real-world data transmission experiments show that the added latency is reasonable, less than 21 5

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/17/2022

IoT Device Identification Based on Network Traffic Characteristics

IoT device identification plays an important role in monitoring and impr...
research
12/28/2018

Clippy(ing) Network Functions: Towards Better Abstractions for Checking and Designing Network Programs

When programming network functions, changes within a packet tend to have...
research
11/29/2021

Network Traffic Shaping for Enhancing Privacy in IoT Systems

Motivated by privacy issues caused by inference attacks on user activiti...
research
04/28/2021

Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks

The distributed inference framework is an emerging technology for real-t...
research
10/21/2021

Classification of Encrypted IoT Traffic Despite Padding and Shaping

It is well known that when IoT traffic is unencrypted it is possible to ...
research
06/09/2020

Characterizing IoT Networks with Asynchronous Time-Sensitive Periodic Traffic

This paper develops a novel spatiotemporal model for large-scale IoT net...
research
12/01/2021

DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels

In edge-cloud collaborative intelligence (CI), an unreliable transmissio...

Please sign up or login with your details

Forgot password? Click here to reset