Identifying Compromised Accounts on Social Media Using Statistical Text Analysis

04/19/2018 ∙ by Dominic Seyler, et al. ∙ University of Illinois at Urbana-Champaign 0

Compromised social media accounts are legitimate user accounts that have been hijacked by a third (malicious) party and can cause various kinds of damage. Early detection of such compromised accounts is very important in order to control the damage. In this work we propose a novel general framework for discovering compromised accounts by utilizing statistical text analysis. The framework is built on the observation that users will use language that is measurably different from the language that a hacker (or spammer) would use, when the account is compromised. We use the framework to develop specific algorithms based on language modeling and use the similarity of language models of users and spammers as features in a supervised learning setup to identify compromised accounts. Evaluation results on a large Twitter corpus of over 129 million tweets show promising results of the proposed approach.

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1. Introduction

Account compromising (a.k.a. account hijacking) has become a major issue for public entities and regular users alike. One of the most prominent examples is the hostile takeover of the Associated Press Twitter account in April 2013 (Lee, 2013). A tweet which claimed an attack on the White House caused a stock market panic and major stock indexes to fall, as well as a wakening of the US-dollar. Also in 2013, another news report found that over a quarter million accounts on Twitter were compromised (Kelly, 2013). Despite massive efforts to contain account hacking it is still an issue until today. More recently, public entities that suffered an attack include ABC news (Park, 2017), McDonald’s (Ciaccia, 2017), Amnesty International, Forbes and others (Russell, 2017). Shockingly, the Times magazine reported that Russian hackers were able to compromise a large number of Twitter accounts for the purpose of spreading misinformation in the 2016 U.S. Presidential Election (Calabresi, 2017). This incidents are prime examples of the severity of damage that hackers can cause when their messages are backed by the trust that users put into the entity, whose account was compromised. For regular users a compromised account can be a very embarrassing experience, since often questionable posts are published with their identity in social media. As a result, 21% of users that fall victim to an account hack abandon the social media platform (Thomas et al., 2014).

Compromised accounts are legitimate accounts that a malicious party takes control over, with the intention of gaining financial profit (Thomas et al., 2014) or spreading misinformation (Egele et al., 2017). These accounts are especially interesting for hackers/spammers, since they can exploit the trust network that the legitimate user has established (Ruan et al., 2016; Egele et al., 2017). For example, this exploitation makes it more likely that users within the personal network of the compromised account click on spam links, since they erroneously believe that their source is legitimate. Finding these hijacked accounts is especially challenging, since they exhibit similar traits than regular accounts (Ruan et al., 2016). For example, the user’s personal information, friends, likes, etc. are all legitimate at the time of the account takeover. (Thomas et al., 2014) found that the detection of an account takeover can take up to five days, with 60% of the takeovers lasting an entire day. A time window this large will give hackers ample time to reach their goal. Furthermore, simple matching against a black list cannot be used in the compromised account detection task. Only after analyzing the changes in the account’s behavior, patters can be identified that expose the account as being compromised. This task therefore requires methodology that is specific to accounts that have been compromised, as opposed to regular spam accounts.

In this work we introduce a novel framework for compromised account detection that is based on the observation that a regular user’s textual output will differ from a spammer’s textual output. When we further assume that an account is only compromised once 111This assumption is not essential as our general idea is also applicable when an account is compromised more than once.

, we can separate the user’s text from the spammer’s by defining a begin and end of an account takeover. As a specific implementation of the framework, for a user account under consideration, we model the user’s and spammer’s language as two smoothed multinomial probability distributions that are estimated using the textual output of the user and spammer, respectively. We can then use a similarity measure between probability distributions as indicators that an account is being compromised. Even though we do not know the begin and end of an account takeover our method leverages the fact that the average difference for random account begin and end dates will be higher for compromised accounts as for benign accounts. Using features derived from the similarity measure, we can then train a classifier that can reliably detect compromised accounts.

A major challenge in our evaluation is to find the ground truth in a social media dataset. Finding compromised accounts manually within an enormous dataset is clearly infeasible and there is no dataset publicly available that contains such information. We therefore introduce an approach where we artificially create compromised user accounts, by injecting spam into these accounts. Spam, in our case, is simulated by switching part of a user’s post stream with another random user’s posts. Using this methodology we gain full control over the ground truth and can vary such parameters as amount of accounts compromised and begin/end of an account takeover. To show applicability in a real-world environment, we find evidence that non-artificially compromised accounts can be detected when the algorithm is trained on synthetic data.

2. Related Work

Existing work on compromised user accounts can be divided into two broad categories: Category one are papers that present methods for compromised account detection, which we label as “detection” (Egele et al., 2013; Trang et al., 2015; Ruan et al., 2016; Egele et al., 2017). The second category strives to deeper investigate characteristics of accounts that are known to be compromised, which we label as “analysis” (Murauer et al., 2017; Zangerle and Specht, 2014; Thomas et al., 2014; Grier et al., 2010). The entire amount of works belonging to the detection category is manageable having only three distinct works. We take this as evidence that this problem has great potential for new findings in terms of methodology specific to the problem domain. A similar observation has been made in (Adewole et al., 2017), a recent survey paper, and in (Trang et al., 2015; Ruan et al., 2016).

A study that profiled compromised accounts (Thomas et al., 2014), found that hackers gain control of these accounts either directly, by finding user name and password information, or through an application by using a valid OAuth token. In the case where compromised accounts are exploited for financial gains, (Thomas et al., 2014) found three major schemes, namely: (1) The sale of products, e.g., weight loss supplements. (2) The sale of influence in the social network, e.g., a compromised account would advertise the sale of followers and simultaneously it would be used to generate these “fake” followers. (3) Lead generation, where users were tricked into filling surveys.

The work that is closest to our problem setting is (Egele et al., 2013) and a near-identical version of the paper in terms of methodology (Egele et al., 2017). There, the authors learn behavioral profiles of users and look for statistical anomalies in features based on temporal, source, text, topic, user, and URL information. However, compared to our method the textual features are superficial, since they only consider the change in language (e.g., from English to French) of messages. Also, topic features are somewhat primitive, since they only consider message labels, such as hashtags, as topics. Thus, there is a large potential for deeper semantic and/or statistical analysis of the textual content of social media posts to detect these compromised accounts. To the best of our knowledge, there exists no approach that has utilized temporal statistical characteristics of language for compromised account detection.

The major contribution of (Trang et al., 2015) is a novel evaluation framework, where user accounts are artificially compromised by switching out part of two users’ tweet streams. This allows for complete control over the ground truth and thereby potentially enabling the generation unlimited training and testing data. In addition to the proposed evaluation framework, the authors investigate reasons for the sparseness of research on compromised account detection as compared to fake account detection. They further improve the method proposed in (Egele et al., 2013), such that it can detect the hijacking event of an account earlier.

The work (Ruan et al., 2016)

performs and extensive study of behavioral patterns using click-stream data on Facebook. In general, it is hard for academics to obtain such click data, since they are usually only accessible for the social media provides themselves or through a full-fledged user study. From the observations made in the behavioral study, the authors derive features that model public (e.g., posting a photo) and private (e.g., searching for user profiles) behavior of users. To find compromised accounts the authors look at different click-streams associated with a single user and calculate feature variance. An account is considered compromised if the difference of a new click-stream does not fall within the variance estimated on the user’s click-stream history. Here as well, the authors use data (i.e., click-streams) of other users to simulate an compromised account. Also, their method seems to be tailored to social media platforms that are similar to Facebook. In contrast, our method is general enough that it can theoretically be applied to any social media platform that uses text (e.g., Twitter, Facebook, LinkedIn, etc.)

Another work that was shown to be applicable to compromised account detection is (Viswanath et al., 2014)

. There, the authors use principal component analysis (PCA) to characterize normal user behavior, which allows them to distinguish it from abnormal behavior. Their features do not consider text and it is noticeable that compared to other detection categories, their approach performed worst on compromised account detection. An interesting contribution is the generation of ground-truth data for detecting compromised accounts. There, they reverse-engineer a Trojan that can perform certain actions on a user’s social media account incognito. They then monitor actions that were send by a server that controls the Trojan and flag the accounts that posted a suspicious message as compromised.

The work that is closest to ours methodologically is (Martinez-Romo and Araujo, 2013). There, the authors use language models estimated on a tweet containing a URL, the URL page’s content and other tweets related to trending topics using KL-Divergence. The focus of this work is to find spam tweets in isolation and therefore the applicability to compromised account detection is not obvious. A further shortcoming of the approach is that it is limited to tweets that contain URLs.

Our work shares a similar goal with work on privacy protection in information retrieval (e.g., (Shen et al., 2007; Arampatzis et al., 2011; Fang et al., 2016; Zhang and Yang, 2017), and work in a recent workshop on the topic (Yang et al., 2016)) but with a different focus on detecting compromised user accounts rather than protecting the private information reflected in user’s search queries.

3. Problem Definition

Before discussing our framework in detail we define the problem formally. Let be the set of all users. Further, let be a tweet from user at time . Our goal is to find all compromised user accounts , where a compromised account for user is denoted as .

For classification we turn the compromised account detection task into a binary classification problem. Here, the goal is to decide for each user whether it is compromised or not. We therefore learn a function , which returns 1 if and 0 otherwise.

One possible way to frame this problem is to decide whether for every . This means that for every new tweet that a user sends we have to decide whether the account is compromised or benign. This certainly represents the best case from an interactive system point-of-view. However, this would require to re-train the classifier for all to make an updated prediction, which is computationally infeasible for large datasets. Even if the classifier is re-trained only periodically, the computational cost is still enormous for datasets greater than 100,000 users.

There are definitely trade-offs between desirable practicability and scalability. In our work we decided to treat this problem as a binary classification problem that is decided on a per-user basis. We argue that this case delivers the best balance between practicability and scalability.

4. Approach

Figure 1. Overview of approach.

4.1. A General Framework

We now turn to describe the proposed framework for identifying compromised accounts. As mentioned before, the framework is based on the assumption that a spammer’s textual output will deviate significantly from a regular user’s textual output. Another assumption our framework makes is that an account is compromised only once, which covers the majority of account hijacking incidents.

To capture this discrepancy between language usage, we propose to divide the tweet space of a user into two non-overlapping sets and . We randomly assign two timepoints: signals the start of the account takeover and signals its end. All with make up . All with make up .

Now we can measure the difference between and using any similarity measure of our choice. This procedure can be repeated multiple times for different values of and . can be of different granularity, where the minimum is per-post and the maximum can be chosen at will. The more often the procedure is repeated for a certain user, the higher is the sampling rate. Higher sampling rates make for better approximations of the true difference between and . This strategy thus provides a flexible tradeoff between accuracy and efficiency.

The similarity measures can then be employed as features in the downstream task. For example, if the similarity for a certain user passes a threshold . This threshold can be learned in the training phase, such that the number of classified compromised accounts that match the labeled compromised accounts is maximized. Naturally, any other useful features (if available) can also be further added to improve accuracy.

4.2. Instantiation with Language Modeling

In what follows, we describe our practical instantiation of the framework using language modeling and supervised learning. We create a classifier that can distinguish compromised from benign user accounts based on a set of features derived from our framework. We measure this as the difference between two word probability distributions of a user and a spammer. We select KL-divergence (Kullback and Leibler, 1951) as our method of choice to compare the difference of probability distributions.

More specifically, we assume that when a user writes a tweet she draws words from a probabilistic distribution that is significantly different from the distribution a spammer draws words from. Let and be two word probability distributions (i.e., language models) for the user and spammer, respectively. We therefore need to select two time points and that mark beginning and end of the account takeover by a spammer. As described by our framework, all messages that fall within the time interval will contribute to the spammer’s language model , whereas will contribute to .

Figure 1 presents an overview of our approach for distinguish compromised from benign accounts. For a particular user, we sample her tweet stream for random , pairs. As seen in the figure, the algorithm can, for example, select and as and , respectively. Thus, all tweets that fall between contribute to . All tweets that fall in contribute to . Then, the KL-divergence for these specific and contributes one sample for this user. This process is repeated for different sample rates (i.e., 5, 10, 25, 50), where each time and are selected at random, with constraint . The sample rates are selected based on a feasibility analysis that we describe in Section 5

. As features for our classifier we select the maximum, minimum, mean and variance of the sampled KL-divergence scores. These features are then combined in a logistic regression model, which learns the optimal weighting for each feature based on the training data. The classifier is then evaluated on held-out data to measure its performance (Section

6).

4.3. Language Modeling Details

When dealing with language models, we try to estimate the joint probability of for all words

in the text. According to the chain-rule, this is equivalent to computing

. Because of the combinational explosion of word sequences and the extensive amount of data needed to estimate such a model, it is common to use n-gram language models. N-gram language models are based on the Markov assumption that a word

only depends on previous words (). The simplest and computationally least expensive case is the uni-gram. Here, the Markov assumption is that a word is independent of any previous word, i.e., .

All for make up a language model , which is a multinomial probability distribution where each distinct word in the document is an event in the probability space. The parameters for the language models are estimated using maximum-likelihood. It can be shown that maximizing the likelihood of the uni-gram language model is equivalent to counting the number of occurrences of and dividing by the total word count (, where is the word count of ). The proof is out of the scope of this work and therefore omitted.

This distance between two probability distributions can be estimated using Kullback-Leibler-divergence

(Kullback and Leibler, 1951). In the discrete case, for two multinomial probability distributions and the KL-divergence is given in Equation 1. From the equation it can be observed that . However, it is common practice to still think of as a distance measure between two probability distributions (Zhai and Massung, 2016).

(1)

Another issue with is that the sum runs over , which is the event space of and . Thus, it requires the event space to be equivalent for both distributions. In our case is a uni-gram language model (i.e. a multinomial probability distribution). Now, let denote the vocabulary set of . As a result of maximum-likelihood estimation, in most cases . Thus, we have to smooth the probability distributions such that , which is required in order to calculate . To achieve this, we define . Then, we set and and estimate each multinomial distribution of and using the Laplace estimate.

5. Experimental Design

This section discusses five research questions that we answer experimentally. We first perform a feasibility analysis to find evidence that (I) compromised user accounts do exhibit higher difference in language models compared to benign accounts. To show this we calculate KL-divergence for all possible combinations of and for a subset of users and manually investigate the characteristics of these accounts.

The second part of our feasibility analysis finds proof that (II) the average KL-divergence can be estimated by randomly sampling a certain number of points with different begin/end dates. For this we leverage the same data as in our first feasibility study and plot, for different sample rates, the estimated average KL-divergence and mean squared error, compared to the actual averages.

Following the feasibility study, we show (III) how effective our method is in detecting compromised accounts in a simulated environment. We perform a metrical evaluation where we train and test our algorithm on synthetic data and use standard metrics (i.e. Accuracy, Precision, Recall, -score) for performance measure. We further investigate (IV) how long it takes for our method to classify an account as compromised. For this we plot the percentages of compromised accounts that where correctly identified against an ascending number of injected tweets. This is done as to approximate the number of tweets it takes for the classifier to make an accurate prediction.

The final question we answer is (V) how effective is our method on a real (non-synthetic) dataset. For this purpose we perform an empirical evaluation, where we train our algorithm on synthetic data and evaluate on the original data. Since in this case there are no classification labels available, we perform a manual evaluation. We will now first introduce our dataset, followed by the experimental design, followed by presentation and discussion of the results and ending in an error analysis.

5.1. Dataset

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Algorithm 1 Create dataset with artificially compromised user accounts.

As mentioned earlier, since there are no public datasets available that are applicable to our task, we decided to simulate account hijackings by injecting spam into regular user accounts. For simulation we leverage a large Twitter corpus of roughly 467 million posts from 20 million users covering a seven month period from (Yang and Leskovec, 2011). The dataset only contains textual, temporal and user information. Relationships between users, tweets, etc. are unknown.

Since this dataset was created for a different purpose, it has no information about which Twitter accounts are actually compromised. Thus, a major challenge is to create a gold standard which labels accounts as compromised. Finding these accounts manually within a dataset of 20 million users is clearly infeasible. On the other hand, if we use an existing approach to label the ground truth automatically we would assume this other method to be perfect. A fair comparison to our approach would be rendered impossible.

Simulating account hijackings enables us not only to create a gold standard but also to have full control over such parameters as amount of accounts compromised and begin/end of account takeover. For dataset creation we follow an approach inspired by (Trang et al., 2015), where part of the tweets of two user accounts are swapped to artificially create an compromised account. (Trang et al., 2015) argues that these artificially compromised accounts are often harder to detect than actually compromised accounts, since these posts do not necessarily contain keywords or URLs that can be usually found in spam posts.

In (Trang et al., 2015) the date for an account takeover is chosen at random but always ends with the last tweet of the account. We go one step further and choose the beginning and end at random, meanwhile ensuring that only a certain pre-defined fraction of the tweets is compromised. This allows us to test the effectiveness of our method in different scenarios where different percentages of tweets are compromised within an account. Our algorithm for dataset creation works as outlined in Algorithm 1. Variables and fall in range and specify how many user accounts are compromised and what percentage of tweets is compromised in a user account, respectively.

5.2. Feasibility Studies

In our feasibility studies we use a subset of the data by selecting 495 users at random, where each compromised account contains 50% compromised tweets. For each user we put all tweets into daily buckets and calculate the KL-divergence for all possible combinations of and . The reason this cannot be done for the whole dataset is simply because of computational cost. We therefore operate on this subset to investigate whether our method is feasible and can be approximated to avoid increased computational effort.

5.3. Metrical Evaluation

Our metrical evaluation aims to show the performance of the algorithm in numerical terms, meaning by using standard metrics for performance measure. For testing how well the classifier performs, we need binary labels that indicate whether an account is compromised. Since this kind of dataset is not publicly available we decided to simulate the account hacking. Using this methodology we can generally generate as much training and testing data as needed.

For our experiments we utilize the dataset we created as described in Section 5.1. To get meaningful estimates for the language models and we select the 100,000 users with most tweets in our data. Our resulting set of users have a maximum of 86,593 and a minimum of 571 tweets. Furthermore, we filter users with less than 10 days of coverage, resulting in our final dataset of 99,697 users with 129,442,756 tweets in total.

In our first study we investigate the performance of our classifier using various metrics. We report a confusion matrix with details on class-based performance. Furthermore, we measure Precision, Recall and

-score for the class representing the predictions of compromised accounts (1-labels). For training we select 67% of user accounts and for testing 33% of user accounts at random. In this experiment 50% of the tweets of a compromised account are compromised. We also perform an ablation study where features are tested in ensemble and in isolation.

In addition, we show the effectiveness of our method for different levels of difficulty in our second study. We experiment with various settings for the percentage of compromised tweets in compromised accounts with random begin and end dates of account take over. More concretely, we select 50%, 25%, 10% and 5% of tweets to be compromised, each representing a more difficult scenario. Further, we employ 10-fold cross validation to get the most representative results, ensuring that each data point has been utilized for testing. In both experiments the probability of an account being compromised is set to to obtain a balanced dataset.

As mentioned before we perform a third study, with the goal to approximate how long it takes for our method to classify an account as compromised. Here we combine all accounts with varying percentages of tweets compromised (i.e. 50%, 25%, 10% and 5%) and group them by the actual number of tweets that where injected. We then plot the percentages of compromised accounts that where correctly identified for 0 to 600 injected tweets with bucket sizes of 50.

5.4. Empirical Evaluation

Our empirical evaluation aims to show that once the algorithm is trained on artificially compromised accounts, it can detect compromised accounts in the original dataset. To achieve this we manually evaluate the accounts that have the highest probability according to our classifier, which was trained on the synthetic data.

For our experiments, the dataset is similar as described in the previous section. However, one major change is that we only inject spam into the training dataset. We randomly select 70 of users for training and 30 of users for testing. In this experiment 25 of tweets of a compromised account in the training dataset are compromised.

Since there is no ground truth for accounts in the testing dataset, we define four specific metrics to evaluate whether an account is considered as compromised or not. The four metrics are listed as following: (1) The account has a specific and sharp topic change in certain time periods. (2) The account has a specific tweet posting frequency change in certain time periods. (3) The account has a specific language change in certain time periods. (4) The account posts highly repeated tweets or similar contents in certain time periods. If two or more of those metrics are met, an account is considered as being compromised.

6. Experiment Results

6.1. Language Model Similarity of Compromised Accounts

(a) Benign user account
(b) Benign user account
(c) Compromised user account
(d) Compromised user account
Figure 2. KL-divergence heatmap for different benign (Figure 1(a) and 1(b)) and compromised user accounts (Figure 1(c) and 1(d)). The x-axis of each figure depicts different values for , while the y-axis depicts different values for . The color palette ranges from blue (low KL-divergence) to red (high KL-divergence).

In the first part of our feasibility analysis we answer research question (I) and examine whether any significant differences in KL-divergence between regular (benign) and compromised user accounts exist.

To achieve this we manually inspect the differences in user accounts by leveraging a heatmap as a visual cue. Figure 2 depicts four heatmaps of two benign (Figure 1(a) and 1(b)) and compromised user accounts (Figure 1(c) and 1(d)). The x-axis of each figure depicts different values for , while the y-axis depicts different values for . The color palette ranges from blue (low KL-divergence) to red (high KL-divergence). The left part of the plot below the diagonal is intentionally left empty, since these values would represent nonsensical variable assignments for and .

It is immediately obvious that we find more high KL-divergence values for compromised compared to benign accounts. In the figures high values are expressed by red and dark red colors. From this observation it can be inferred that the average KL-divergence will be higher for accounts that are compromised. By manually inspecting over 100 of these user account heatmaps we find that many of them follow this general trend. We understand this as preliminary evidence that a method which utilizes KL-divergence for detecting compromised accounts is feasible.

We further noticed in Figures 1(c) and 1(d) that the account takeover happened where the KL-divergence reaches its maximum (see the dark circle in Figure 1(c) and tip of the pyramid in Figure 1(d)). Unfortunately, this is not true for all inspected accounts, but it hints that there might be potential to find the most likely period of an account takeover with our current framework. This could be done by finding and that maximizes the difference of the language models and .

6.2. Estimate KL-divergence Using Random Sampling

In our second feasibility analysis we investigate research question (II), whether the average KL-divergence of a user account can be approximated using random sampling. More specifically, we try to find a reasonable estimate by calculating the KL-divergences only for a subset of the and pairs.

Since we have calculated the KL-divergence for every possible combination of and for all of the 495 users, we know the actual average KL-divergence. We then try different sampling rates. For every sampling rate we average over the samples and compare them to the actual average that is calculated using all , pairs.

The result is shown in Figure 3. In the figure we plot the actual average KL-divergence for compromised (comp) and benign against the averaged samples (sample) for different sample rates. The sample rates range from 1 to 311. The left y-axis shows the averaged KL-divergence. The plot shows that the average KL-divergence for compromised accounts is about 0.1 higher than for benign accounts. This confirms our findings in Section 6.1. Furthermore we see that for small sample rates () there are minimal deviations for the average (). The higher the sample rate the lower these deviations become, as our estimate gets better.

Since our estimates only deviate slightly we also investigate the mean squared error (mse) for each sampling rate. Here, the mse is defined as:

In Figure 3 we plot the mse for compromised and benign accounts. The values for mse are plotted on the right y-axis. For very small sampling rates () we see errors of over 0.07 and over 0.06 for compromised and benign accounts, respectively. Once the sample rate is greater than 120 the mse is close to 0. We therefore conclude that a sampling rate in the range of [50, 120] is sufficient for our experiments.

Figure 3. Results for different sample rates in interval . The left y-axis shows the averaged KL-divergence, the right y-axis shows the mean squared error (mse).

6.3. Effectiveness on Synthetic Data

In this subsection we show (III) how effective our method is in detecting compromised accounts in a simulated environment and investigate (IV) how long it takes for our method to classify an account as compromised.

In Figure 4 we present a confusion matrix that measures performance of our classifier on held-out data on a 67%/33% train/test dataset split. From the matrix we can see that the classifier performs slightly better in classifying benign accounts. In total, 13,308 compromised accounts are identified correctly and 3,222 compromised accounts remain misclassified.

Table 1 presents the the ablation study with different evaluation measures for the same data. It shows the performance of all features in ensemble and individually. We observe that we gain maximum performance over all metrics when all features are utilized (see column “Ensemble”). For both classes the classifier reaches an accuracy of 0.85. When focusing on the class of compromised accounts only, the classifier achieves high Precision of 0.88, with slightly lower Recall at 0.80. Both measures are combined in the -score, which reaches 0.84.

When features are investigated individually, we find that the features “Max” and “Variance” perform equally low, with “Variance” having better recall. It is noteworthy that for both Recall is higher than Precision, even though for the ensemble the opposite holds true. This might be an indicator that these features contribute meaningful signals to the overall model, that improve Recall overall. The features “Min” and “Mean” also perform almost equally in isolation and much better than the other two. We find it surprising that the minimum sample of the two probability distribution is such a strong signal. We hypothesize that this might be due to the fact that the minimum will be higher for compromised accounts when the sample rate is small and most and dates fall within the actual range of the begin and end of an account hijacking.

Measure Ensemble Max Min Mean Variance
Accuracy 0.85 0.58 0.77 0.75 0.56
Precision 0.88 0.58 0.82 0.77 0.55
Recall 0.80 0.59 0.70 0.70 0.70
0.84 0.59 0.75 0.73 0.61
Table 1. Ablation study using different measures of classifier performance on held-out data with 67%/33% train/test split. Features are tested in ensemble and individually.
Figure 4. Confusion matrix for evaluation on held-out data. Compromised accounts have class label 1, benign accounts have class label 0.

The results for our next experiment can be observed in Table 2

. As expected, best performance is reached when 50% of tweets in a compromised account are compromised, with accuracy at 0.85. When the number of compromised tweets is reduced by half the accuracy drops by 0.1 to 0.75. When the amount of compromised tweets is set to 10% and 5% accuracy further reduced to 0.63 and 0.57, respectively. In all scenarios a small confidence interval (

) is observed, showing that each fold barely deviates from the average.

Compromised % Accuracy
Table 2. Ten-fold cross validation results for different percentages of compromised tweets in an account, measured in accuracy with 95% confidence interval.

To answer research question (IV), how long it takes for our method to classify an account as compromised, we approximate the number of tweets it takes for the classifier to make an accurate prediction. In Figure 5 we plot the percentage of correctly identified compromised accounts against an ascending number of injected tweets. We can see that for a small number of tweets [0, 49], our method identifies about 42% of compromised accounts correctly. Then, a general upwards trend in performance is observable until the maximum is reached at 600 tweets at about 76%. From this we can conclude that our method is less effective for a small amount of injected tweets, which makes it less suitable for real-time detection tasks. It is, however, more effective for accounts that carry a modest to large amount of spam tweets, which makes our method more suitable for retrospective analysis of the data.

Figure 5. Percentages of compromised accounts that were correctly identified (y-axis) for 1 to 600 injected tweets with bucket sizes of 50 (x-axis).

6.4. Effectiveness on Real Data

The final question we answer experimentally is (V), how effective is our method on a real (non-synthetic) dataset. In our first step we categorize accounts with the highest probability of being compromised into six categories news, advertisement/spam, re-tweet bot, compromised, regular users, unknown . The results can be found in Table 3. We find that most of these accounts belong to categories with high variation in language, i.e., news, advertisement/spam, re-tweet bot. One of the accounts is found to be compromised. The algorithm also misclassified seven user accounts as being compromised. One account was categorized as “unknown”, since it was posting in a language that none of the evaluators speaks. These results show that our algorithm can also be used to detect “unusual” accounts and users, thus potentially enabling development of novel text mining algorithms for analyzing user behavior on social media, which should be a very interesting future research topic.

Category % Count
News
Advertisement/Spam
Re-tweet Bot
Compromised
Regular User
Unknown
Table 3. Category assignment of manually evaluated accounts.

We further inspect the current (as of December 2017) state of each account. We found a total of five different states (i.e., abandoned, active, deleted, tweets protected and suspended). Table 4 lists the results. We find that most accounts are abandoned, which is not surprising given the age of the dataset. Noteworthy is also that one of the accounts was suspended by Twitter. The account that was identified as compromised was set to tweets protected, which could be an indicator that said user had become more conscious about tweets that were posted from her account and therefore decided to not share her tweets publicly.

Account Status % Count
Abandoned
Active
Deleted
Tweets protected
Suspended
Table 4. Current account status of manually evaluated accounts (Dec 2017).

6.5. Compromised Account Profiling

We now further investigate the compromised account and discuss the details which made us believe that a compromise had happened. While manually investigating the account’s tweets they where mostly discussing celebrities, movies and music, with very few links in any messages. At some point in the tweet stream a near-duplicate message is posted hundreds of times with only brief pauses between tweets. The pattern that all tweets had in common is shown in Figure 6. Here, @{username} were different users, that were most likely followers of the account. With this scheme the hacker tries to directly grab the attention of a targeted user. The placeholder {link}, was one of two links that were identified as suspicious by the link-shortening service the hacker utilized to hide the actual URL. Unfortunately, both links do not exist anymore. We therefore cannot provide additional details about the target of these links. After this “attack” of messages, the tweets return to a similar pattern as before.

From our standpoint we therefore conclude that this account was compromised for the following reasons: (1) The account was discussing different topics before the attack. (2) The account was posting few links in messages. (3) A near-duplicate message with suspicious intend and links was repeatedly posted hundreds of times at a certain point. (4) These messages stop and the account continues with regular activity. From the content of this messages we conclude that the hacker was pursuing a led generation scheme (Thomas et al., 2014), where users are lured into clicking a link that will offer some sort of payment for their information. It is reasonable to assume that if our algorithm were applied at much larger scale to all the Twitter users, it would most likely be able to detect many more compromised accounts.

Pattern: @{username} as discuss [sic] earlier heres [sic] the link, another service that will pay $ for your tweets, anyone can join {link}

Profit scheme: Lead generation

Current status: Tweets protected

Figure 6. Profiling of compromised account.

6.6. Error Analysis

From Table 3, we can see that five news accounts, four advertisement/spam accounts and two re-tweet bots are misclassified as compromised accounts. We meet this problem because our method uses a similarity function, in this case the KL-divergence, as the measurement to determine whether an account is being compromised or not. One common characteristic of news accounts and advertisement accounts is that they rarely use the same words or sentence patterns because there are different news and products everyday. Thus, the high language diversity in these accounts results in the KL-divergence between any two time periods to be high, which leads to the misclassification.

A potential way to improve the method would be to order the samples chronologically instead of treating them as a set. An algorithm could then look for extreme changes in KL-divergence, which should mostly occur in compromised accounts and less in news or spam accounts, where the KL-divergence is constantly high.

The regular user accounts do not exhibit any obvious pattern for the misclassification. We found that some of these misclassified accounts do exhibit one or more of the following characteristics that could potentially influence the KL-divergence samples: (1) excessive usage of re-tweets (2) lots of messages with distinct links (3) lots of repeated messages (4) many misspellings of the same word. Further exploration of this is another very interesting future research direction.

7. Discussion and Future Work

We proposed a novel general framework based on statistical text analysis for detecting compromised social media accounts. Following the framework, we proposed a specific instantiation based on uni-gram language models and KL-divergence measure, and designed features accordingly for use in a classifier that can distinguish compromised from benign accounts. We address the challenge in evaluating this task by developing a method to create a synthetic dataset with compromised user accounts, where we inject spam into the artificially hacked user accounts. Our metrical and empirical experiments on a large Twitter corpus validated the efficacy of our method, showing that the algorithm can detect compromised accounts with up to 85% accuracy when trained and tested on synthetic data. Moreover, we find evidence that our method can identify “real”’ compromised accounts when trained on synthetic and tested on the original data.

Our work makes a novel connection between security and information retrieval and opens up many interesting new research direction where we can potentially apply many information retrieval and text mining techniques to address the security challenges in social media. Below we briefly discuss some of the interesting future research directions enabled by our work:

Ranking user accounts: Instead of framing the problem as binary classification, we can aslo frame it as ranking user accounts in descending order of the likelihood of being compromised and study how to develop such ranking algorithms. Such a ranked list could be passed to human experts to sequentially examine and determine whether there are compromised account within this list. This would allow us to create a pool of accounts with relevance labels, e.g. {0 := not compromised, 1 := shows weak indication of compromise, 2 := shows strong indication of compromise, 3 := is compromised without doubt } that mirror the experts opinion about the severity of indicators that an account is compromised. These labels can then be utilized as a test collection to compare the ranking accuracy of different compromised account detection approaches.

More features for classification: The classifier can easily use many other features that we can construct based on text analysis. For example, one can look at the whole stream of the tweets for a user and examine the time series of term frequencies for each term to see if there’s any interruption. Further, it would be advantageous to incorporate features that utilize the social graph. One could, for example, incorporate the reaction of friends into a model, or make use of other social network-based statistics, such as the number of re-tweets. Although we didn’t explore these directions, the flexibility and extensibility of the proposed framework would allow us to plug in more features to potentially further improve the accuracy.

Deeper semantic analysis: A potential limitation of our approach is the relatively simple modeling of language. Extending our approach could be envisioned by adding a notion of topics to the user model. Each user could then be represented as a mixture of topics for different time points. In this scenario, another signal for an account takeover would be if a user deviates significantly from her topic cluster. In the same context, our uni-gram model does not take word ambiguity or word similarity into account. Therefore, a user model that contains many ambiguous or semantically close words might be more accurate if these words are grouped together. One concept that appears useful for this task might be word embeddings. On the other hand, reducing the granularity of the user’s vocabulary too drastically might result in the model becoming too general and therefore failing to accurately model the individual user’s characteristics.

Improve evaluation: When creating compromised user accounts artificially, our approach uses tweets of another legitimate user account. (Trang et al., 2015) argues that this makes the problem harder than necessary, since the language of two users might by less different than of a user and a spammer. We therefore propose to inject real spam messages into the user accounts instead of tweets of regular users. One could also think of a generative method that is trained on regular spam messages. Once trained, this method could generate as much unique (i.e. unseen) spam as needed.

Forensic Analysis: For detailed forensic analysis it might be required to find the most likely period of an account takeover. We realized that there is a potential of doing so with our current framework. We believe that this could be achieved by choosing and such that they maximize the difference between the user’s and spammer’s language. Finding the maximum efficiently is a hard problem, since it’s computationally expensive to compute the difference for all and pairs. Therefore, an approximation algorithm would be required.

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