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Abstract <p>In the realm of hyper-connected world, social media’s widespread use has given rise to a new class of cyber threats known as social media botnets. As botnets become increasingly sophisticated in mimicking humans, there is a growing need to develop dedicated detection techniques to address the evolving threats. This paper introduces <i>BotWard</i>, a novel social network-based botnet detection framework. The goal is to combat social media botnets by constructing a resilient system that can maintain control and detect malicious activity despite botnet evasion techniques. <i>BotWard</i> constructs command and control servers (C&amp;C Servers) within a social network, each assigned a unique pseudo-random nickname. The botmaster establishes C&amp;C channels using hidden commands embedded inside diaries through an information-hiding mechanism. An automatic reconfiguration mechanism enhances the framework’s resilience by providing early warning and automatic recovery of crippled C&amp;C communications. Additionally, a zombie nickname detection method is proposed to detect pseudo-random zombie nicknames in batches with high accuracy, leveraging lexical feature differences between authentic and fake nicknames. Experiments show that <i>BotWard</i> can promptly resume C&amp;C communication and Maintain a 100% control rate, even after complete C&amp;C server failures. The zombie nickname detection method demonstrated high accuracy (98.9%) with a low false alarm rate (2%). By addressing the challenges posed by social network-based botnets, <i>BotWard</i> significantly enhances network security. Its practical and robust countermeasures provide effective solutions for detecting and mitigating social botnet threats.</p> Graphical abstract <p></p>

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BotWard: A resilient framework for detecting and mitigating botnets in complex social networks through pseudo-random nickname identification

  • 期刊论文

  • 发表日期：

    2025年10月18日

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  • Riaz Ullah Khan,
  • Hanan Aljuaid,
  • Dawar Khan,
  • Rajesh Kumar

摘要

Abstract

In the realm of hyper-connected world, social media’s widespread use has given rise to a new class of cyber threats known as social media botnets. As botnets become increasingly sophisticated in mimicking humans, there is a growing need to develop dedicated detection techniques to address the evolving threats. This paper introduces BotWard, a novel social network-based botnet detection framework. The goal is to combat social media botnets by constructing a resilient system that can maintain control and detect malicious activity despite botnet evasion techniques. BotWard constructs command and control servers (C&C Servers) within a social network, each assigned a unique pseudo-random nickname. The botmaster establishes C&C channels using hidden commands embedded inside diaries through an information-hiding mechanism. An automatic reconfiguration mechanism enhances the framework’s resilience by providing early warning and automatic recovery of crippled C&C communications. Additionally, a zombie nickname detection method is proposed to detect pseudo-random zombie nicknames in batches with high accuracy, leveraging lexical feature differences between authentic and fake nicknames. Experiments show that BotWard can promptly resume C&C communication and Maintain a 100% control rate, even after complete C&C server failures. The zombie nickname detection method demonstrated high accuracy (98.9%) with a low false alarm rate (2%). By addressing the challenges posed by social network-based botnets, BotWard significantly enhances network security. Its practical and robust countermeasures provide effective solutions for detecting and mitigating social botnet threats.

Graphical abstract