Performance evaluation of cybercriminal detection through cluster computing techniques

The cybercrime has evolved as the years ago and the criminals use different techniques to capture the victims. Researchers have devised various techniques to detect the cybercriminal. Hence, a new technology has to be used to detect the Cybercrime. From the literature, it is analysed that many researchers have used various techniques to detect the Cybercrime. In this paper, the performance evaluation of various cybercriminal detection techniques are analysed using cluster computing techniques and Matlab is used to determine their performance. In this paper, 25 attributes are analysed to detect the cybercriminal through various techniques such as Gaussian Clustering, K Means, Fuzzy C Means and Fuzzy Clustering. Some of the attributes are taken as varying attributes and some of the attributes are taken as the nonvarying attributes. The crime clusters and genuine clusters are determined. The reasons for choosing the various attributes and the various techniques are also given. The analysis is further strengthened by changing the number of attributes used in each technique i.e., 25 attributes, 15 attributes, 10 attributes and 5 attributes. The analysis is further continued by using ¾ of total attributes, ½ of total attributes and ¼ of total attributes. The efficiency and time complexity is determined. In the paper justification of a number of attributes selected in each technique is also given. The accuracy analysis is done by comparing with Gaussian Clustering with K Means Clustering technique, Fuzzy C Means, and user profile analysis. The attributes which are significantly contributing for identifying the criminals are determined in each technique.