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Machine learning plays a vital role in the current world. In the case of an Intrusion detection system to classify the normal and the malicious packet, the machine learning classification algorithms are used. When the live packets are captured and classified without allowing it to enter the network or the host represents a proactive intrusion detection System. Every machine learning algorithm has its pros and cons. This article works on the KDD dataset to classify the captured packets using the machine learning-based classification algorithms of
Decision tree and Random forest. The implementation work with Denial of Service attack, Man in the middle attack, and buffer overflow attack. The attack packets are captured and classified using Random forest and decision tree and based on that, the classification algorithms are also compared with Accuracy, precision, and recall parameters.
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