Defending against social engineering attacks: A security pattern‐based analysis framework - Tong Li; Chuanyong Song; Qinyu Pang (IET Information Security)
Abstract Social engineering attacks are a growing threat to modern complex systems. Increasingly, attackers are exploiting people's "vulnerabilities" to carry out social engineering attacks for malicious purposes. Although such a severe threat has attracted the attention of academia and industry, it is challenging to propose a comprehensive and practical set of countermeasures to protect systems from social engineering attacks due to its interdisciplinary nature. Moreover, the existing social engineering defence research is highly dependent on manual analysis, which is time‐consuming and labour‐intensive and cannot solve practical problems efficiently and pragmatically. This paper proposes a systematic approach to generate countermeasures based on a typical social engineering attack process. Specifically, we systematically ‘attack’ each step of social engineering attacks to prevent, mitigate, or eliminate them, resulting in 62 countermeasures. We have designed a set of social engineering security patterns that encapsulate relevant security knowledge to provide practical assistance in the defence analysis of social engineering attacks. Finally, we present an automatic analysis framework for applying social engineering security patterns. We applied the case study method and performed semi‐structured interviews with nine participants to evaluate our proposal, showing that our approach effectively defended against social engineering attacks.