DATA MINING FOR MALICIOUS CODE DETECTION SYSTEM

Abstract

Data mining is the process of posing queries and extracts the patterns into the unknown from the previously located large quantities of data using pattern matching or other reasoning techniques. These patterns can be seen as details of the input data, and may be used for to analyzing in machine learning and predictive analytic of data. Data mining has many applications in security including for national security as well as for cyber security. The threats to national security include hijacking destroying critical infrastructures such as power grids and telecommunication systems also the E-commerce. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan, malware, spyware, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. Data mining is also becomes other applications include data mining for malicious code detection such as worm detection and managing firewall policies. The various types of threats to national security and describe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats [5].Data mining is also being applied for credit card fraud detection and biometrics related applications. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. The root kit records were categorized as Inline and other based on the attribute values. In this paper, we proposed three algorithms [10] named as RIPPER [10], Navies Bayes approach, and Multi-Naive Bayes using data mining techniques and the comparison of these algorithms. The various types of threats and then discuss the applications of data mining for malicious code detection, cyber security and national security.