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An Adaptive Weighted Voting Classifier for Botnet Detection in Internet of Things

The growing importance of the Internet of Things in applications and services has made it an attractive source of security attacks. The recent threat landscape involves sophisticated botnet attacks involving IoT devices. The proposed work is focused on developing an adaptive voting classifier that can detect all the attack categories in BoT-IoT dataset with higher performance scores. The hyperparameters of the base classifier are tuned so that each classifier delivers the highest prediction score. The tuned classifier is fed to the voted classifiers through an adaptive voting scheme to get the highest performance scores. The accuracy, precision, recall, and F1 score are found to be significantly better than the existing works in this area. The detection rate for all classes except theft is found to be in the range of 0.99-1 and for the theft category, it is found to be 0.95. The true positive rate is close to 1 for all classes and for theft category it is 0.95. The FNR rate for all classes including theft, the smallest category is zero which is impressive. The proposed method can detect all attack categories in a highly imbalanced dataset without relying on resampling techniques.