Attack Detection Using Decision Tree and Naïve Bayes with Bat Algorithm-Based Feature Selection

Currently, several algorithms in machine learning (ML) are employed to enhance the detection of attacks on networks. The developers have suggested several solutions for the detection of attacks on networks in the previous works. This paper contributes to the earlier works by employing the Decision Tree (DT) and Naïve Bayes (NB) algorithms on the UNSW-NB15 dataset. In addition, the Bat Algorithm (BA) metaheuristic optimizer is used to lessen the irrelevant and redundant features. The achievement of the DT and NB algorithm classifiers is evaluated in terms of accuracy, Precision, and Recall. The DT achieved 100 % Accuracy, Precision, and Recall. On the other hand, the NB achieved $99.81 \%, 99.87 \%$, and 99.91 % Accuracy, Precision, and Recall, respectively. The results show that DT achieved the optimal results with the BA algorithm, and NB DT achieved nearly optimal results.