Intrusion Detection and Violation of Compliance by Monitoring the Network
Keywords:
Intrusion Detection, Navie Bayes Algorithm, Spam Filtering, Dynamic Tuning MechanismAbstract
Network and security of system has vital role in data communication environment. Web services and networks can be crashed on attempting many possible ways on forwarding by hackers or intruders. It causes malicious rapt in which it needs a technique called Intrusion Detection System through Spam Filtering. Thus gives the protection to networks. It can be done by using Open Source Network Intrusion Detection System called Snort. The process of arranging the e-mail with framed criteria called Spam Filtering. Proposed System, a Machine Learning Algorithm called Simple Probabilistic Navie Bayes Classifier used to detect the intrusion. Based on its content Probability of Spam messages can be calculated in Navie Bayes Classifier by learning it from spam and Good mail which results a robust, efficient anti-spam approach and adaption. Sniffing the packet and Fed it as input to Navie Bayes Classifier will give Test Dataset. Depends on spam and intrusion probability, the email is been classified as good or spam.
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