A new Approach of Detecting Internet Worm
Internet worm means separate malware computer programs that repeated itself and in order to spread one computer to another computers. Malware includes computer viruses, ransom ware, worms, Trojan ourse, root kits, key loggers, and dialers, spyware, adware, malicious, BHOs, rogue security software and other malicious programs. It is programmed by attackers to disrupt computer operation, gather Sensitive Information, or gain access to private computer systems. It can appear in the form of code, scripts, active content, and other Software. We need to detect a worm in the internet, because it may be create network vulnerabilities and also it will reduce the system performance. We can detect the various types of Internet worm the worm like, Port scan worm, Udp worm, http worm, User to Root Worm and Remote to Local Worm. In existing process it is not easy to detect the worm, there is difficult to detect the worm process. In our proposed systems, internet worm is a critical threat in computer network. Internet worm is self propagating, and fast spreading. We need to detect the worm and classify the worm using data mining algorithm. For using data mining machine learning algorithm like Random Forest, Decision Tree, Bayesian Network we can effectively classify the worm in internet.
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