Deep Learning Algorithms for Multiple Cyberattacks Detection

Abstract

Data is pervasive and accessible through the internet. The proliferation of smart devices worldwide, such as computers and mobile phones, has led to a significant increase in internet usage. Consequently, this surge has also given rise to a corresponding increase in cyberattacks, which are a prevalent issue faced by internet users. To address this problem, it is crucial to have an effective cyberattack detection mechanism in place to safeguard computer networks, systems, and data. While intrusion detection systems (IDS) play a significant role in this regard, they do have their limitations. Therefore, in this research, two deep learning algorithms, namely Multilayer Perceptrons (MLPs) and Recurrent Neural Networks (RNN), have been proposed. The NSL-KDD
and CIC-IDS-2017 datasets were utilized for this project. When using the NSL-KDD dataset, the MLP algorithm achieved an accuracy of 99.44% with a false positive rate of 0.52%, whereas the RNN algorithm achieved an accuracy of 98.02% with a false positive rate of 2.21%. On the other hand, when employing the CIC-IDS-2017 dataset, the MLP algorithm achieved an accuracy of 99.98% with a false positive rate of 2.06%, while the RNN algorithm achieved an accuracy of 99.09% with a false positive rate of 39.65%. Furthermore, various metrics such as precision, recall, F1-score, error rate, and others were calculated and compared for both models. The obtained results clearly indicate that the MLP algorithm outperformed the RNN algorithm in terms
of performance when applied to both datasets