Intrusion Detection in Computer Networks using Decision Tree and Feature Reduction

Today, the need for anomaly-based intrusion detection systems is felt more than ever due to the emergence of new attacks and the increase in Internet speed. The main criterion for determining the validity of an efficient intrusion detection system is the detection of attacks with high accuracy. In addition to inability of existing systems to manage growing attacks, also they have high rates of positive and negative misdiagnosis. This paper uses the ID3 decision tree features for anomaly-based intrusion detection systems. Two feature selection methods are also used to reduce the amount of used data for the detection and categorization. The KDD Cup99 dataset was used to evaluate the proposed algorithm. The test results show a detection accuracy of 99.89% for the DoS attack and an average accuracy of 94.65% for all attacks using the decision tree, indicating better values ​​than previous tasks.