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.
|Number of pages||29|
|Journal||Transactions on Emerging Telecommunications Technologies|
|Early online date||16 Oct 2020|
|Publication status||Published - Jan 2021|
Bibliographical note© 2020 The Authors. Transactions on Emerging Telecommunications Technologies published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
FunderFunding Information: This research is fully funded by Research, Innovation and Enterprise Centre (RIEC), Universiti Malaysia Sarawak under the grant number F08/PGRG/1908/2019.
- Deep Learning
- Machine Learning
- Network Anomaly Detection
- Network Intrusion System
- Network security
- Deep learning
- Network intrusion detection system
- Machine learning
- Network anomaly detection
ASJC Scopus subject areas
- Electrical and Electronic Engineering