The Hacker Is Inside the Company! - Instance-Based Learning And Its Application to Defend Against Insider Hacking
David Esquivel, Shawn Trousdale, Mohammad Ariful Islam Khan, Palvi Aggarwal, Deepak K. Tosh · 2024
This research was conducted by the authors while participating in the Cybersecurity Research Experience for Educators through Data Science (CREEDS), a Research Experience for Teachers (RET) summer program funded by the U.S. National Science Foundation at the University of Texas at El Paso (UTEP). The work explores the application of Instance-Based Modeling to understand the behavior of cyber attacker in order to ensure better defense. The study simulates the hacker choices among multiple targets with various reward/penalty structures. The fact that the common elements from human decision-making such as cognitive noise and memory decay influences the model has also been discussed. Ultimately, the authors propose strategies on how these techniques can be introduced in high school class rooms in order to make a bridge between advanced cybersecurity research and practical secondary math and computer science curricula.