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Survey of intrusion detection systems: techniques, datasets and challenges

Cybersecurity (Singapore), 2019-07, Vol.2 (1), p.1-22, Article 20 [Peer Reviewed Journal]

The Author(s) 2019 ;The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2523-3246 ;EISSN: 2523-3246 ;DOI: 10.1186/s42400-019-0038-7

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  • Title:
    Survey of intrusion detection systems: techniques, datasets and challenges
  • Author: Khraisat, Ansam ; Gondal, Iqbal ; Vamplew, Peter ; Kamruzzaman, Joarder
  • Subjects: Anomaly detection ; Computer Science ; Computer security ; Datasets ; Intrusion ; Intrusion detection system ; Intrusion detection systems ; Machine learning ; Malware ; NSL_KDD ; Security management ; Survey ; Taxonomy
  • Is Part Of: Cybersecurity (Singapore), 2019-07, Vol.2 (1), p.1-22, Article 20
  • Description: 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.
  • Publisher: Singapore: Springer Singapore
  • Language: English
  • Identifier: ISSN: 2523-3246
    EISSN: 2523-3246
    DOI: 10.1186/s42400-019-0038-7
  • Source: Open Access: DOAJ Directory of Open Access Journals
    SpringerOpen
    AUTh Library subscriptions: ProQuest Central
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