Detection of criminally convicted email users by behavioral dissimilarity

Abstract

The phenomenon of social interactions is prevailing charismatically like a spider net in nowadays society despite the people busy lives. In this fashion, people willingly supply their private or public data without sensing the threat of any information theft. These kinds of information could be easily misused and could be analyzed by any third party for malicious or non-malicious purposes. In this paper, detection of irregular or anomalous individual are focused. Individual with behavioral dissimilarity are discovered and validated with the real denounced victims. An affluent feature set of 15 characteristics is anticipated for deviation detection. The kth nearest neighbour technique is applied on the Enron dataset for finding accused email users. Noteworthy outputs are achieved by implication of the KNN method.