Computer technology has advanced to a greater extent which leads to increase in cyber crime committed in recent years. The detection of cyber crime is not an easy task. From the literature, many researchers used various technologies to detect the cyber crime. In this paper, performance evaluation of various techniques are analysed to determine the cyber criminal. Firstly the detection of synthetic identity theft is checked. Secondly, the intrusion detection is checked using the honey pot security mechanism. Thirdly, the detection is further strengthened using the lie detection technique where the false speech of a person is determined. Finally by analysing the user profile, the detection of cyber crime is done using the clustering techniques. Synthetic Identity Theft method performs better than the remaining methods when considered for evaluation. Experimental results show that comparison of the final list of criminal users and the list of criminals determined, the number of genuine users eliminated are 41 out of 100 users, where as the number of genuine users eliminated from other methods are 16, 36 and 38 only. The number of attributes used is only 4, where as the number of attributes used for other methods are 5, 10 and 25. The percentage of performance metrics is also 37.1 and gradient is 31.1 which are better compared to other methods considered for performance analysis.
The current greatest challenge to Network security is cyber warfare. Every part of computer network security is an essential component. In the existing technologies computer networks and systems are more crucially concerned with network security. The greatest challenge in network is addressing security to the server side. This latest end-to-end research paper recommends to improve the security performance to protect the network from intruders. An advanced honeypot based Intrusion Detection technique is used to detect and analyze threats to ensure security. The Honeypot technique adds a layer to the network security to enhance its performance. A key feature of honeypot is to distract the attacker from the real system to derive important information about hacker activities. To validate whether the clients are authorized or unauthorized and monitor the unauthorized client activities, as a first step we use IP validation together with vulnerability detection of user activities. In the second step we use voice recognition as detectors of malicious attacks. Once IP validation and voice recognition are processed and matched then the client is an authorized client and the packet is transmitted to the server, but if not matched it is an unauthorized client, then the packet transmits it to the honeypot server. Our experiment has demonstrated that this particular approach can successfully identify unknown attacks with greater than 95% detection rate and less than 1% false alarm rate.
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