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Effective Features Selection and Machine Learning Classifiers for Improved Wireless Intrusion Detection

Machine learning algorithms are effective means applied to wireless intrusion detection systems (WIDS) in an attempt to protect computing resources against unauthorized access. A key aspect for an improved WIDS based on machine learning classification is features selection. This paper considers multiclass classification that utilizes four effective features sets of 32, 10, 7 and 5 features, respectively. The classes represent 15 types of 802.11 MAC layer attacks, and the features represent 802.11 frame fields information. The experimental results utilized the Aegean Wi-Fi Intrusion Dataset (AWID) to evaluate the performance of seven well-known machine learning classifiers, namely, AdaBoost, Random Forest, Random Tree, J48, logit Boost, Multi-Layer Perceptron, and ZeroR with respect to the selected features set. The presented work outperforms previous related work in terms of number of classes, features and accuracy. The proposed system using the Random Forest algorithm and 32 features achieves a maximum accuracy of 99.64%. By using logit Boost with five features, we achieved a maximum accuracy of 99.53%.