Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection Using Weka
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For the past few years, it has been seen that the computer intrusion attacks are becoming more sophisticated, and the volume, velocity, and variance of traffic data have greatly increased. Because the conventional methods and tools have become impotent in the detection of intrusion attacks, most intrusion detection systems now embrace the use of machine learning tools and algorithms for efficiency. This is because of their ability to process large volume, velocity, and very high variance data. This work reviews and analyzes the performance of three out of the most commonly used machine learning algorithms in network intrusion. In this work, the performance of Naïve Bayes, decision tree, and random forest algorithms were evaluated as they were being trained and tested with the KDD CUP 1999 dataset from DARPA using a big data and machine learning tool called Weka. These classification algorithms are evaluated based on their precision, sensitivity, and accuracy.
Intrusion detection , Algorithm , Machine learning , Supervised learning , Research Subject Categories::TECHNOLOGY::Information technology
Azeez, N.A (2018). Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection Using Weka.