Finite State Machine for the Social Engineering Attack Detection Model: SEADM

Abstract

Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. Human operators are one of the weakest links in the security chain as they are highly susceptible to manipulation. 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 with respect to formal definitions, attack frameworks, and examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying abstract finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been shown to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the inter-connections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. The finite state machine is then verified by applying it to representative social engineering attack scenarios from all three streams of possible communication. This verifies that all the capabilities of the SEADM are kept in tact, whilst being improved, by the proposed finite state machine.