Enhanced Detection of Social Bots on Online Platforms using SemiSupervised K-Means Clustering
DOI:
https://doi.org/10.69996/jsihs.2024002Keywords:
Online social networks, botnet, machine learning, social bots, semi-supervised clustering algorithmAbstract
Social bots are semi-automatic or automatic computer applications that express human performance in OSN. Social bots are the primary tools utilized by hackers to invade OSNs. The current use of Social bots in communication and voting operations has been highlighted. Twitter and Tumblr have been efficiently used to share information about public sentiment. The developing connection on the Internet has started up avenues for improved cybersecurity threats and perpetuation of an extensive array of cybercrimes occurring in significant financial needs and user data privacy violations. One of the most advanced but critical extensions to the public of malicious software is the bot malware, commonly referred to as botnets. The most current presentation techniques of malicious social bots examine the quantitative characteristics of their behavior. This paper proposed a novel approach to identifying malicious social bots, including feature determination based on the development probability of clickstream progressions and the Semi-supervised K-Means Clustering algorithm for detecting social bots. The proposed method explains the transition probability of user behavior clickstreams and reflects the time feature. The proposed Semi-supervised K-Means Clustering (SSKMC) algorithm, compared with the traditional detection method based on the quantitative function, improves accuracy by 15% on average. The proposed SSKMC Algorithm can efficiently detect malicious accounts on social bots.
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