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A Supervised Feature Selection Method for Malicious Intrusions Detection in IoT Based on Genetic Algorithm


Saman Iftikhar, Daniah Al-Madani, Saima Abdullah, Ammar Saeed, Kiran Fatima


Vol. 23  No. 3  pp. 49-56


Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique ? Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.


Evolutionary computing; Genetic Algorithm (GA); Internet of Things; BoT-IoT dataset; Malicious Intrusions; Support Vector Machine (SVM); Logistic Regression (LR).