Detecting Anomaly Based Network Intrusion Using Feature Extraction and Classification Techniques
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Abstract
Computer networks are vulnerable to many kinds of cyber-attacks. It is the responsibility of network administrator to protect the data and resources under threat. Network administrators use various techniques like encryption, firewall, intrusion detection system (IDS), etc. to protect the data and resources of the organizations. Intrusion detection is a topic of research and a lot of work has been done in this field. In the present investigation various machine learning classification techniques were applied to the KDD'99 dataset for building anomaly based intrusion detection system. KDD'99 dataset is used as a benchmark in intrusion detection literature.
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