Professor Aunshul Rege

Aunshul Rege, PhD, is an Associate Professor with the Department of Criminal Justice at Temple University, USA. She holds a PhD and MA in Criminal Justice, an MA and BA in Criminology, and a BSc in Computer Science. She has been researching proactive cybersecurity in the context of cybercrimes against critical infrastructures for over 10 years. Specifically, her National Science Foundation funded (CAREER, CPS, EAGER) research examines adversarial and defender behavior, decision-making, adaptations, modus operandi, and group dynamics.

More recently, she has started researching social engineering, which is the psychological manipulation of individuals to start and maintain cyberattacks. Her research has been published in several outlets, such as the Journal of Information Warfare, Journal of Homeland Security and Emergency Management, the Security Journal, and the IEEE Intelligent Systems. Dr. Rege is also passionate about educating the next generation workforce across the social and hard sciences about the relevance of the human factor in cybersecurity through experiential learning.

Expertise

Cybercrime, Social Engineering, Critical Infrastructure, Social Media

Selected Publications

• Rege, A., Mendlein, A. & Williams, K. (forthcoming). “Security and Privacy Education for STEM Undergraduates: A Shoulder Surfing Course Project”. Proceedings of the IEEE Frontiers in Education.
• Rege, A., VanZant, S. (2019). “Examining the Roles of Muhajirahs in the Islamic State via Twitter”. Proceedings of the IEEE Cyber Science Conference. Winner of Best Paper Award.
• Rege, A., Williams, K. & Mendlein, A. (2019). “A Social Engineering Course Project for Undergraduate Students Across Multiple Disciplines”. Proceedings of the IEEE Cyber Science Conference.
• Rege, A. & Adams, J. (2019). “The need for more sophisticated cyber-physical systems war gaming exercises”. Proceedings of the 18th European Conference on Cyber Warfare and Security.
• Rege, A., Williams, K. & Mendlein, A. (2019). “An experiential learning cybersecurity project for multiple STEM undergraduates”. Proceedings from the IEEE Integrated STEM Education Conference.
• Rege, A., Obradovic, Z., Asadi, N., Parker, E., Pandit, R., Masceri, N. & Singer, B. (2018). Predicting Adversarial Cyber Intrusion Stages Using Autoregressive Neural Networks. Special Issue: Data Mining for Cyber Security. IEEE Intelligent Systems 33(2): 29-39.

Course Taught

Undergraduate

CJ 3007: Computer Crime
CJ 3403: Organized Crime
CJ 3000: Media, Crime, and Criminal Justice
CJ 2401: Nature of Crime

Graduate

CJ 8320: Cybercrime
CJ 8310: Surveillance, Security, and Society
CJ 8106: Theories of Crime and Deviance

 

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