Mouton, FLeenen, LVenter, HS2016-08-222016-08-222015-10Mouton, F. Leenen, L. and Venter, H.S. 2015. Social Engineering Attack Detection Model: SEADMv2. In: 2015 International Conference on Cyberworlds, Visby, Sweden, October 2015978-1-4673-9403-1http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7398418&tag=1http://hdl.handle.net/10204/87262015 International Conference on Cyberworlds, Visby, Sweden, October 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's websiteInformation security is a fast-growing discipline, and therefore the effectiveness of security measures to protect sensitive information needs to be increased. Since people are generally susceptible to manipulation, humans often prove to be the weak link in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy as far as formal definitions, attack frameworks, examples of attacks and detection models are concerned. This paper therefore proposes a revised version of the Social Engineering Attack Detection Model. The previous model was designed with a call centre environment in mind and is only able to cater for social engineering attacks that use bidirectional communication. Previous research discovered that social engineering attacks can be classified into three different categories, namely attacks that utilise bidirectional communication, unidirectional communication or indirect communication. The proposed (and revised) Social Engineering Attack Detection Model addresses this problem by extending the model to cater for social engineering attacks that use bidirectional communication, unidirectional communication or indirect communication. The revised Social Engineering Attack Detection Model is further verified using published generalised social engineering attack examples from each of the three categories mentioned.enBidirectional CommunicationIndirect CommunicationSocial EngineeringSocial Engineering Attack ExamplesSocial Engineering Attack Detection ModelUnidirectional CommunicationArticleMouton, F., Leenen, L., & Venter, H. (2015). Social Engineering Attack Detection Model: SEADMv2. http://hdl.handle.net/10204/8726Mouton, F, L Leenen, and HS Venter "Social Engineering Attack Detection Model: SEADMv2." (2015) http://hdl.handle.net/10204/8726Mouton F, Leenen L, Venter H. Social Engineering Attack Detection Model: SEADMv2. 2015; http://hdl.handle.net/10204/8726.TY - Article AU - Mouton, F AU - Leenen, L AU - Venter, HS AB - Information security is a fast-growing discipline, and therefore the effectiveness of security measures to protect sensitive information needs to be increased. Since people are generally susceptible to manipulation, humans often prove to be the weak link in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy as far as formal definitions, attack frameworks, examples of attacks and detection models are concerned. This paper therefore proposes a revised version of the Social Engineering Attack Detection Model. The previous model was designed with a call centre environment in mind and is only able to cater for social engineering attacks that use bidirectional communication. Previous research discovered that social engineering attacks can be classified into three different categories, namely attacks that utilise bidirectional communication, unidirectional communication or indirect communication. The proposed (and revised) Social Engineering Attack Detection Model addresses this problem by extending the model to cater for social engineering attacks that use bidirectional communication, unidirectional communication or indirect communication. The revised Social Engineering Attack Detection Model is further verified using published generalised social engineering attack examples from each of the three categories mentioned. DA - 2015-10 DB - ResearchSpace DP - CSIR KW - Bidirectional Communication KW - Indirect Communication KW - Social Engineering KW - Social Engineering Attack Examples KW - Social Engineering Attack Detection Model KW - Unidirectional Communication LK - https://researchspace.csir.co.za PY - 2015 SM - 978-1-4673-9403-1 T1 - Social Engineering Attack Detection Model: SEADMv2 TI - Social Engineering Attack Detection Model: SEADMv2 UR - http://hdl.handle.net/10204/8726 ER -