Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Cyberattacks have become more complex and pervasive; associated costs are soaring; there is an urgent need for innovative solutions. Socially engineered attacks are escalating in scale, potency, and are increasing in frequency; defenses have not evolved and tactics currently deployed are passive, and arguably offer little deterrent value. Social engineering is rooted in psychology and mediated by technology, therefore, solutions must be informed by a transdisciplinary approach, with the cyber behavioral sciences taking a central role. Identifying and targeting cyberattacker psychological vulnerabilities by means of active cyber defense are under consideration. Automation and scale of response are key requirements, underscoring the need for and the utility of large language models (LLM), in terms of identifying context, scaling to attack type, and generating dialogue to engage the cyberattacker and effectively ‘hack back.’ Hence the present conceptualization of the “HackBot” - an automated strike back innovation, specifically devised to reverse socially engineered attacks in cyber defense contexts.
Recommended Citation
Lundie, Michael; Lindke, Kira; Amos-Binks, Adam; Aiken, Mary; and Janosek, Diane, "The Enterprise Strikes Back: Conceptualizing the HackBot - Reversing Social Engineering in the Cyber Defense Context" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 7.
https://aisel.aisnet.org/hicss-57/da/cyber_deception/7
The Enterprise Strikes Back: Conceptualizing the HackBot - Reversing Social Engineering in the Cyber Defense Context
Hilton Hawaiian Village, Honolulu, Hawaii
Cyberattacks have become more complex and pervasive; associated costs are soaring; there is an urgent need for innovative solutions. Socially engineered attacks are escalating in scale, potency, and are increasing in frequency; defenses have not evolved and tactics currently deployed are passive, and arguably offer little deterrent value. Social engineering is rooted in psychology and mediated by technology, therefore, solutions must be informed by a transdisciplinary approach, with the cyber behavioral sciences taking a central role. Identifying and targeting cyberattacker psychological vulnerabilities by means of active cyber defense are under consideration. Automation and scale of response are key requirements, underscoring the need for and the utility of large language models (LLM), in terms of identifying context, scaling to attack type, and generating dialogue to engage the cyberattacker and effectively ‘hack back.’ Hence the present conceptualization of the “HackBot” - an automated strike back innovation, specifically devised to reverse socially engineered attacks in cyber defense contexts.
https://aisel.aisnet.org/hicss-57/da/cyber_deception/7