Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Rout, Rashmi Ranjan; Lingam, Greeshma; Somayajulu, Durvasula V. L. N.

doi:10.1109/tcss.2020.2992223

Cited by 45 publications

(19 citation statements)

References 46 publications

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“…In the proposed method, we assign DNA bases based on the types and content of tweets shared. Since, these features are proved effective in detecting bots 13 , 14 , 29 , 30 , each tweet posted by a user is assigned a unique DNA base as presented in Table 1 (i.e.) A-plain tweet, T- plain mention, G- plain retweet, C- tweet with media/URLs).…”

Section: Proposed Workmentioning

confidence: 99%

DNA-influenced automated behavior detection on twitter through relative entropy

Gilmary

Venkatesan

Vaiyapuri

et al. 2022

Sci Rep

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Twitter is a renowned microblogging site that allows users to interact using tweets and it has almost reached 206 million daily active users by the second quarter of 2021. The ratio of Twitter bots has risen in tandem with their popularity. Bot detection is critical for combating misinformation and protecting the credibility of online disclosures. Current bot detection approaches rely on the Twitosphere’s topological structure, ignoring the heterogeneity among the profiles. Moreover, most techniques incorporate supervised learning, which depends strongly on large-scale training sets. Therefore, to overcome these issues, we proposed a novel entropy-based framework to detect correlated bots leveraging only user behavior. Specifically, real-time data of users is collected and their online behaviors are modeled as DNA sequences. We then determine the probability distribution of DNA sequences and compute relative entropy to evaluate the distance between the distributions. Accounts with entropy values less than a fixed threshold represent bots. Extensive experiments conducted in real-time Twitter data prove that the proposed detection technique outperforms state-of-the-art approaches with precision = 0.9471, recall = 0.9682, F1 score = 0.9511, and accuracy = 0.9457.

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Section: Proposed Workmentioning

confidence: 99%

DNA-influenced automated behavior detection on twitter through relative entropy

Gilmary

Venkatesan

Vaiyapuri

et al. 2022

Sci Rep

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show abstract

“…The trust accuracy of social bot detection among participants is improved by integrating the trust value of the direct relationship determined by the Bayesian theory and the trust value of the indirect relationship determined by the Dempster-Shafer theory. Rout [8] proposed a learning automata-based malicious social bot detection (LA-MSBD) algorithm integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. Zhang et al [9] proposed a method to combine the old features to obtain more complex features.…”

Section: Social Media Botsmentioning

confidence: 99%

“…This shows that social media bots often send similar tweets, and there may be a situation of batchpublishing identical tweets, so the similarity index of tweets can better distinguish social media bots from normal users. (8) The average length of original tweets is expressed as x i8 : the style of tweets published by normal users and social media bots is not consistent, and the length of relevant tweets is also different, including original tweets and forwarded tweets. As shown in Figure 2h, the average length of tweets sent by normal users and social media bots is counted.…”

Section: Feature Extractionmentioning

confidence: 99%

Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor

Wang

Zheng

et al. 2021

Applied Sciences

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Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and an anomaly detection algorithm. The purpose is to use Variational AutoEncoder to automatically encode and decode sample features. The normal sample features are more similar to the initial features after decoding; however, there is a difference between the abnormal samples and the initial features. The decoding representation and the original features are combined, and then the anomaly detection method is used for detection. The results show that the area under the curve of the proposed model for identifying social media bots reaches 98% through the experiments on public datasets, which can effectively distinguish bots from common users and further verify the performance of the proposed model.

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“…The experimental outcomes revealed the efficiency of the proposed methodology in terms of precision, recall, and F 1 ‐score. In another study, Rout et al 29 suggested an algorithm by combining Learning Automata (LA) and a trusted computational model and applying URL‐based features to diagnose malicious tweets. Furthermore, in 30 researchers combined Whale Optimization Algorithm (WOA) with Salp Swarm Algorithm (SSA) to select an optimum subset of features for Twitter spam profile detection.…”

Section: Background and Related Workmentioning

confidence: 99%

A hybrid classification method for Twitter spam detection based on differential evolution and random forest

Abkenar

Mahdipour

Jameii

et al. 2021

Concurrency and Computation

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Summary Social networking services are online platforms that are distributed across different computers over long distances. Twitter is the most popular microblogging site that allows users to share their opinions and real‐world events. Due to its popularity and ease of use, Twitter has also attracted spammers. As a result, spam detection is one of the most critical problems. In order to provide a spam‐free environment, it is necessary to identify and filter spam tweets as well as their owners. A hybrid method, which is based on Synthetic Minority Over‐sampling TEchnique (SMOTE) and Differential Evolution (DE) strategies, is presented to enhance the spam detection rate in real Twitter datasets. SMOTE is applied to tackle the imbalanced class distribution of datasets, while DE is used to tune Random Forest (RF) hyperparameters. Compared with related work and based on evaluation results, the presented method significantly enhances the classification performance in imbalanced datasets. The detection rate of optimized RF with excellent F1‐score and Area Under the Receiver Operating Characteristic Curve (AUROC), which are 98.97% and 0.999, respectively, demonstrates the high efficiency of the proposed method.

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Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Cited by 45 publications

References 46 publications

DNA-influenced automated behavior detection on twitter through relative entropy

DNA-influenced automated behavior detection on twitter through relative entropy

Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor

A hybrid classification method for Twitter spam detection based on differential evolution and random forest

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