Detecting Malicious Social Bots Based on Clickstream Sequences
Open accessAbstract
With the significant increase in the volume, velocity, and variety of user data (e.g., user-generated data) in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information (e.g., fake news), and this can result in real-world consequences. Therefore, detecting and removing malicious social bots in online social networks is crucial. The most existing detection methods of malicious social bots analyze the quantitative features of their behavior. These features are easily imitated by social bots; thereby resulting in low accuracy of the analysis. A novel method of detecting malicious social bots, including both features selection based on the transition probability of clickstream sequences and semi-supervised clustering, is presented in this paper. This method not only analyzes transition probability of user behavior clickstreams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior clickstreams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.
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Fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots.
“Several authors (Shu et al. 2018b, 2017; Shi et al. 2019; Bessi and Ferrara 2016; Shao et al. 2018a) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016).”
From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Shi et al. 2019
The Abstract (page not supplied) says malicious social bots disseminate false information, including fake news, and the Introduction describes social bots as automated accounts. Neither passage establishes that bots create fake news or that the accounts spreading it have similar attributes and network structure. (LLM, conf=0.85)
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References 28 · Cited by 72
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- [28]28. ZHIYONG ZHANG (M'06-SM'11) received the master's degree from the Dalian University of Technology, and the Ph.D. degree in computer science from Xidian University, China. He is a post-doctoral fellowship at School of Manage- ment, Xi'an Jiaotong University, China. He is cur- rently a full-time Henan Province Distinguished Professor, the Director of the Henan Joint Interna- tional Research Laboratory of Cyberspace Secu- rity Applications, and the Dean of the Department of Computer Science, Information Engineering College, Henan University of Science and Technology. He is also a Visiting Professor with the Computer Science Department, Iowa State University. His research interests include multimedia social networks, digital rights management, trusted computing, and usage control. In recent years, he has published over 80 scientific papers and has edited four books in the above research fields. He holds eight authorized patents. He is a Senior Member of the ACM, the Chair of the IEEE Multimedia Communications Technical Committee DRMIG, a member of the IEEE Systems, Man, Cybermetics Society Technical Committee on Soft Computing, a member of the World Federation on Soft Computing Young Researchers Committee, and a member of the Digital Rights Management Technical Specialist Workgroup Attached to China National Audio, Video, Multimedia System, and Device Standardization Technologies Committee. He is the Chair/Co-Chair and TPC member for numerous international conferences/workshops on digital rights management and cloud computing security. Moreover, he is an Editorial Board Member and an Associate Editor of the IEEE ACCESS, the Multimedia Tools and Applications, the Neural Network World, the EURASIP Journal on Information Security (Springer), and a leading Guest Editor or Co-Guest Editor of the Computer Journal and the Applied Soft Computing and Future Generation Computer Systems.
- [29]29. KIM-KWANG RAYMOND CHOO received the Ph.D. degree in information security from the Queensland University of Technology, Australia, in 2006. He currently holds the Cloud Technol- ogy Endowed Professorship at The University of Texas at San Antonio. He is also a Fellow of the Australian Computer Society. In 2016, he was named the Cybersecurity Educator of the Year - APAC (Cybersecurity Excellence Awards are produced in cooperation with the Information Security Community on LinkedIn). In 2015, he and his team won the Digital Forensics Research Challenge organized by the University of Erlangen-Nuremberg, Germany. He is a recipient of the 2018 UTSA Col- lege of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, the IEEE TrustCom 2018 Best Paper Award, the ESORICS 2015 Best Paper Award, the 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, the Ful- bright Scholarship, in 2009, the 2008 Australia Day Achievement Medallion, and the British Computer Society's Wilkes Award, in 2008.
- Authors
- Shi, P., Zhang, Z., & Choo, K.-K. R.
- Affiliations
- Henan University of Science and Technology (CN)
- The University of Texas at San Antonio (US)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 28855-28862
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2916302242
- Cited by
- 72
- References
- 28
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 11 · 2023: 12 · 2022: 14 · 2021: 11 · 2020: 11 · 2019: 2
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61772174, 61370220)
- Plan For Scientific Innovation Talent of Henan Province (174200510011)
- Program for Innovative Research Team (in Science and Technology) in the University of Henan Province (15IRTSTHN010)
- Program for Henan Province Science and Technology (142102210425)
- Natural Science Foundation of Henan Province (162300410094)
- Henan University of Science and Technology (2015BZCG01)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- bot detection, transition probabilities, user behavior, semi-supervised clustering, fake news, social networking sites
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