Cyber Sentinels: Illuminating Malicious Intent in Social Networks Using Dual-Powered CHAM

Online Social Networks (OSN), the security and reliability of these platforms are extremely vulnerable to malicious users. Online social networks’ volatile extension has amplified the pervasiveness of destructive practices comprising spamming, phishing, and disseminating false information. The administration of dynamic and altering antagonistic strategies has exposed complications in rule-based systems and anomaly detection techniques. Traditional rule-based approaches for detecting malicious behavior often fail to catch multifaceted and emergent threats. The emergent prominence of online social networks has made it essential to progress cutting-edge methods for spotting devious users and preserving network integrity. In this regard, the paper defines a distinctive method CHAM (CNN and Hierarchical Attention Mechanism) that enhances the detection of harmful traffic within these platforms by leveraging Convolutional Neural Networks (CNN) in conjunction with Hierarchical Attention Mechanism (HAM). Amalgam of both techniques enhances the benefit of the detection of malicious users in OSN precisely and efficiently. The model’s fundamental novelty is the adoption of the gated recurrent unit as the primary memory unit, coupled with layers for the attention mechanism, three degrees of maximum pooling, and layers for average pooling. These components work together to extract detailed flow characteristics, making it easier to identify subtle patterns suggestive of malicious behavior. A thorough data preparation phase is carried out before modeling to get precise data flow segments. The proposed framework takes the lead, promising improved detection effectiveness and a safer virtual world for all users. The methodology endeavors to elevate the precision and efficiency of malicious user detection.