Malware Victimization: An Analysis of an Integrated Self-control and Social Learning Model

• In Event: Poster Session

Abstract

Faced with the challenges of protecting consumers against malware attacks, researchers have largely focused on advancing anti-malware technology as preventative measures. Unfortunately, research have shown that a large proportion of malware attacks remain undetected even in light of these advances. As such, there is an apparent need to approach the problem from a different perspective to offer potential solutions that supplements existing anti-malware technology. Towards this goal, this study expands upon existing evidence suggesting that computer users themselves are the weakest link in the security chain. Studies have shown that while anti-malware programs offer some level of protection against known attacks, their effectiveness severely diminishes based on how users act online and their ability to practice good cyber security habits. This study applies self-control theory (Gottfredson & Hirschi, 1990) and social learning theory (Akers, 1998), both well-established theories in the field of criminology in order to model and explain the behavioral processes leading to malware victimization. Criminological theories traditionally have been used to explain terrestrial behavior. Their applicability towards cyber behavior, however, remains largely unexplored. Therefore, results of this study also provides valuable insight into the capacity to which self-control and social learning theories can explain cyber behavior.

Author

• Tsung Martin Tsai, University of New Haven