Network intrusion detection system: A systematic study of machine learning and deep learning approaches

By Zeeshan Ahmad, Adnan Shahid Khan, Wai Shiang Cheah, Johari Abdullah, Farhan Ahmad. Transactions on Emerging Telecommunications Technologies, 2020

1,095 citations recorded by the source corpus.

The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c42c29c793f3b8518a313d03fa101b1201a8ad14

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Citations in the corpus

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “The recent trend in anomaly-based NIDS design favours adopting Deep Learning (DL) algorithms to detect potential network attacks [16].”
Unsolved Problems in ML Safety (2021, background): “For example, systems may use neural networks to detect intruders [4] or malware [173], but if adversaries can modify their behavior to deceive and bypass detectors, the systems will fail.”