Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches

Muhammad Imran Khan; Simon N. Foley; Barry O’Sullivan

doi:10.1007/978-3-030-93956-4_11

Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches

Muhammad Imran Khan
, Simon N. Foley
, Barry O’Sullivan

University College Cork
Norwegian University of Science and Technology

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal information. Database Intrusion detection systems, specifically behaviour-based database intrusion detection systems, have been shown effective in detecting insider attacks. This paper presents background concepts on database intrusion detection systems in the context of detecting insider threats and examines existing approaches in the literature on detecting malicious accesses by an insider to Database Management Systems (DBMS).

Original language	English
Title of host publication	Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers
Editors	Weizhi Meng, Sokratis K. Katsikas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	178-197
Number of pages	20
ISBN (Print)	9783030939557
DOIs	https://doi.org/10.1007/978-3-030-93956-4_11
Publication status	Published - 2022
Event	2nd International Symposium on Emerging Information Security and Applications, EISA 2021 - Virtual, Online Duration: 12 Nov 2021 → 13 Nov 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1403 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Symposium on Emerging Information Security and Applications, EISA 2021
City	Virtual, Online
Period	12/11/21 → 13/11/21

Keywords

Anomaly detection systems
Behaviour-based anomaly detection systems
Insider threat
Intrusion Detection Systems
Relational Database Management Systems

Access to Document

10.1007/978-3-030-93956-4_11

Cite this

Khan, M. I., Foley, S. N., & O’Sullivan, B. (2022). Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches. In W. Meng, & S. K. Katsikas (Eds.), Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers (pp. 178-197). (Communications in Computer and Information Science; Vol. 1403 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-93956-4_11

Khan, Muhammad Imran ; Foley, Simon N. ; O’Sullivan, Barry. / Database Intrusion Detection Systems (DIDs) : Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches. Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers. editor / Weizhi Meng ; Sokratis K. Katsikas. Springer Science and Business Media Deutschland GmbH, 2022. pp. 178-197 (Communications in Computer and Information Science).

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abstract = "One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal information. Database Intrusion detection systems, specifically behaviour-based database intrusion detection systems, have been shown effective in detecting insider attacks. This paper presents background concepts on database intrusion detection systems in the context of detecting insider threats and examines existing approaches in the literature on detecting malicious accesses by an insider to Database Management Systems (DBMS).",

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Khan, MI, Foley, SN & O’Sullivan, B 2022, Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches. in W Meng & SK Katsikas (eds), Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1403 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 178-197, 2nd International Symposium on Emerging Information Security and Applications, EISA 2021, Virtual, Online, 12/11/21. https://doi.org/10.1007/978-3-030-93956-4_11

Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches. / Khan, Muhammad Imran; Foley, Simon N.; O’Sullivan, Barry.
Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers. ed. / Weizhi Meng; Sokratis K. Katsikas. Springer Science and Business Media Deutschland GmbH, 2022. p. 178-197 (Communications in Computer and Information Science; Vol. 1403 CCIS).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

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KW - Relational Database Management Systems

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Khan MI, Foley SN, O’Sullivan B. Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches. In Meng W, Katsikas SK, editors, Emerging Information Security and Applications - 2nd International Symposium, EISA 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 178-197. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-93956-4_11