Machine Learning Methods for Network Intrusion Detection
This work addresses the need for effective intrusion detection systems to maintain network security, but it is incremental as it applies existing methods to a standard dataset without introducing new techniques.
The study tackled the problem of network intrusion detection by evaluating machine learning classifiers on the KDD dataset, finding that the J48 classifier achieved the highest accuracy for detecting and classifying attacks such as DOS, R2L, U2R, and PROBE.
Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.