Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation
This work addresses social engineering threats for cybersecurity applications, but it is incremental as it builds on existing lexical methods.
The paper tackles the problem of detecting social engineering attacks and generating responses by developing an extensible lexicon based on Lexical Conceptual Structure, focusing on 'ask' and 'framing' concepts, and demonstrates improvements in detection performance and qualitative response generation.
We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation. We leverage the central notions of ask (elicitation of behaviors such as providing access to money) and framing (risk/reward implied by the ask). We demonstrate improvements in ask/framing detection through refinements to our lexical organization and show that response generation qualitatively improves as ask/framing detection performance improves. The paradigm presents a systematic and efficient approach to resource adaptation for improved task-specific performance.