Survey of intrusion detection systems: techniques, datasets and challenges

By Ansam Khraisat, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman. Cybersecurity, 2019

1,689 citations recorded by the source corpus.

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes. It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/38e81867da601ae6877dd9ce2c1e1aa085852ecb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Abdallah Moubayed
2. Li Yang
3. Abdallah Shami
4. Ammar Alazab
5. Ansam Khraisat

In Computer Networks and Communications

1. Ammar Alazab
2. Ansam Khraisat
3. Li Yang
4. Abdallah Shami
5. Abdallah Moubayed

In Signal Processing

1. Ammar Alazab
2. Ansam Khraisat
3. Li Yang
4. Abdallah Shami
5. Abdallah Moubayed

Citations in the corpus

• MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, background): “For DoS, gear spoofing, and RPM spoofing attack types, the proposed system can reach 100% detection rates, very low false alarm rates (0.0%-0.449%), and very high F1-scores [84].”
• A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, methodology): “Likewise, Khraisat, et al. (Khraisat et al., 2020) experimented with both single and ensemble classifiers composed of the decision tree, and SVM, for classification of the NLS KDD intrusion detection evaluation data set.”