The sound of intrusion: A novel network intrusion detection system. (December 2022)

• Record Type: Journal Article
• Authors: Aldarwbi, Mohammed Y.; Lashkari, Arash H.; Ghorbani, Ali A.
• Is Part Of: Computers & electrical engineering. Volume 104:Part A(2022)
• Publication Date: 2022-12
• Subjects: Intrusion detection systems -- IDS -- Anomaly detection -- Deep learning -- CNN -- DBN -- LSTM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108455
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A network intrusion detection system is an essential part of network security research. It detects intrusion behaviors through active defense technology and takes emergency measures such as alerting and terminating intrusions. To this end, with the rapid development of learning technology, various machine-learning-based and deep-learning-based approaches have been developed, but there is a limitation in the detection accuracy. We believe that dealing with network traffic as if they are vibrations, waves, or sounds would allow us to detect intruders better. In this work, we envisioned a novel Network Intrusion Detection System called "the sound of intrusion". The proposed system transforms the traffic flow features into waves and utilizes advanced audio/speech recognition deep-learning-based techniques to detect intruders. We used several deep-learning-based techniques including long short-term memory, deep belief networks, and convolutional neural networks. The proposed approach has been validated using two well-known and recent benchmark datasets namely NSLKDD and CIC-IDS2017. It achieves the highest detection accuracy, 84.82%, and 99.41%, with the lowest false alarm rate of 0.12% and 0.004% on two common network intrusion detection systems datasets, namely NSL-KDD and CICIDS2017, respectively. It demonstrates improvements over existing approaches, and shows a strong potential for use as a modern Network Intrusion Detection System. Graphical abstract: Highlights: Deep-learning-based NIDS has been proven to be successful and effective. Network flow features have been considered as sounds . Highest detection accuracy with the lowest false alarm have been achieved.