Explaining Cybercrime through the Lens of Differential Association Theory, Hadidi44-2.php PayPal Case Study

Social learning theories, such as differential association theory, state that criminals develop deviant behaviors and learn the tools of their trade through close association with other deviants. This case study examines a group of 99 email addresses found to be using the same PayPal phishing kit. It uses Open Source Intelligence techniques to determine potential relationships between the holders of these email addresses. The results are then discussed in light of differential association theory to determine the extent to which this theory may aid in the understanding of cybercrime.

[1]  Zhao Yong-Xia,et al.  MD5 Research , 2010, 2010 Second International Conference on Multimedia and Information Technology.

[2]  Gary Warner,et al.  Analysis of Back-Doored Phishing Kits , 2011, IFIP Int. Conf. Digital Forensics.

[3]  L. Lanza-Kaduce,et al.  Social learning and deviant behavior: a specific test of a general theory. , 1979, American sociological review.

[4]  Ronald L. Rivest,et al.  The MD5 Message-Digest Algorithm , 1992, RFC.

[5]  Robert G. Morris,et al.  CRACKING THE CODE: AN EMPIRICAL EXPLORATION OF SOCIAL LEARNING THEORY AND COMPUTER CRIME , 2009 .

[6]  Weider D. Yu,et al.  A phishing vulnerability analysis of web based systems , 2008, 2008 IEEE Symposium on Computers and Communications.

[7]  Guofei Gu,et al.  BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection , 2008, USENIX Security Symposium.

[8]  Joseph B. Kadane,et al.  Using uncleanliness to predict future botnet addresses , 2007, IMC '07.

[9]  Calton Pu,et al.  Evolutionary study of phishing , 2008, 2008 eCrime Researchers Summit.

[10]  Vidyasagar Potdar,et al.  Spammer and hacker, two old friends , 2009, 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[11]  Gary Warner,et al.  Phishing: Crime that pays , 2011, 2011 eCrime Researchers Summit.

[12]  Tina L. Freiburger,et al.  The Internet as a Terrorist's Tool: A Social Learning Perspective , 2011 .

[13]  Justin Zhan,et al.  Phishing in Finance , 2010, 2010 5th International Conference on Future Information Technology.

[14]  Thorsten Holz,et al.  As the net churns: Fast-flux botnet observations , 2008, 2008 3rd International Conference on Malicious and Unwanted Software (MALWARE).

[15]  R. Iannotti,et al.  Cyber and traditional bullying: differential association with depression. , 2011, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[16]  Ram Dantu,et al.  Behavior analysis of spam botnets , 2008, 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE '08).

[17]  Marti A. Hearst,et al.  Why phishing works , 2006, CHI.

[18]  R. Agnew.  Testing the Leading Crime Theories: An Alternative Strategy Focusing on Motivational Processes , 1995 .

[19]  Chuanxiong Guo,et al.  Online Detection and Prevention of Phishing Attacks , 2006, 2006 First International Conference on Communications and Networking in China.

[20]  Kathryn C. Seigfried-Spellar,et al.  Self-Reported Internet Child Pornography Consumers: A Personality Assessment Using Bandura’s Theory of Reciprocal Determinism , 2011 .

[21]  Robert L. Akers,et al.  A Differential Association-Reinforcement Theory of Criminal Behavior , 1966 .

[22]  Brad Wardman,et al.  The Deadliest Catch: Reeling In Big Phish With a Deep MD5 Net , 2010, J. Digit. Forensics Secur. Law.