Hi, I’m Aaqil, a PhD researcher in Computer Science at the University of Texas at El Paso (UTEP) (started in 2025). I completed my bachelor’s under Anna University in 2024, where I developed a strong interest in research and applied problem-solving. Before starting my PhD, I worked as a Software Developer at Brainvault Technologies, supporting multiple U.S. clients and building production-grade software systems and data workflows. My prior publications span research in Artificial Intelligence, Machine Learning, and Data Analytics, with applied work in agriculture and predictive modeling.

My research focuses on behavioral cybersecurity, specifically adversarial decision making and how human cognitive biases shape attacker behavior. While cybersecurity often models attackers as purely rational and technically optimal, real adversaries make decisions under uncertainty, limited information, time pressure, and cognitive constraints. I study how biases such as the Sunk Cost Fallacy, Default Effect, and related decision heuristics influence attackers’ persistence, escalation, risk tolerance, and strategy switching during cyber operations. A central question in my work is not only what attackers do, but why they continue, abandon, or escalate certain actions even when evidence suggests a different choice would be more effective.

At UTEP I examine behavioral signals such as time investment, switching decisions, repeated attempts, escalation patterns, and action sequences, and translate these into measurable constructs for rigorous analysis. Using statistical modeling, comparative testing, and cluster based methods, I identify consistent behavioral profiles and quantify how different cognitive factors combine to produce bias driven outcomes. Rather than treating attacker behavior as uniform, my work emphasizes heterogeneity showing how distinct decision-making styles emerge even under the same technical conditions.

A key insight driving my research is that technical skill does not necessarily eliminate bias. Even skilled adversaries can become vulnerable to decision traps when prior effort, perceived progress, or default paths distort judgment. By grounding security research in empirically observed behavior, I aim to build models of adversarial cognition that are realistic, explainable, and actionable. Ultimately, my goal is to translate these findings into psychologically informed, bias aware cyber defense mechanisms adaptive strategies that anticipate predictable decision tendencies and strengthen security through a more human centric understanding of attackers.