Intrusion detection systems (IDSs) must be implemented across the network to identify and avoid attacks to counter the emerging tactics and techniques employed by hackers. In this research, we propose a lightweight IDS for improving IDS efficiency and reducing attack detection execution time. We use an RF algorithm to rank features in order of importance and then reduce data dimension by selecting the top 15 important features. We then implement a deep neural network (DNN) architecture to classify anonymous traffic by analyzing TCP/IP packets on the network security laboratory-knowledge discovery in databases (NSL-KDD) dataset. The results indicate that our proposed technique of applying RF to identify important features can improve the DNN-based IDS system’s execution time and performance and has low latency for intrusion detection as compared to other state-of-the-art techniques.
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