A4 Article in conference proceedings
Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring (2024)

Shelke, P., & Hämäläinen, T. (2024). Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring. In M. Lehto (Ed.), Proceedings of the 23rd European Conference on Cyber Warfare and Security (23, pp. 748-755). Academic Conferences International Ltd. Proceedings of the European Conference on Cyber Warfare and Security. https://doi.org/10.34190/eccws.23.1.2123

JYU authors or editors

Publication details

All authors or editors: Shelke, Palvi; Hämäläinen, Timo

Parent publication: Proceedings of the 23rd European Conference on Cyber Warfare and Security

Parent publication editors: Lehto, Martti

Conference:

Place and date of conference: Jyväskylä, Finland, 27.-28.6.2024

ISBN: 978-1-917204-06-4

eISBN: 978-1-917204-07-1

Journal or series: Proceedings of the European Conference on Cyber Warfare and Security

ISSN: 2048-8602

eISSN: 2048-8610

Publication year: 2024

Publication date: 21/06/2024

Volume: 23

Issue number: 1

Pages range: 748-755

Number of pages in the book: 847

Publisher: Academic Conferences International Ltd

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.34190/eccws.23.1.2123

Persistent website address: https://papers.academic-conferences.org/index.php/eccws/issue/view/33

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/96198

Abstract

The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, encompassing diverse approaches such as machine learning, artificial intelligence, behavioral analysis and anomaly detection. Machine learning plays a central role in cyber threat detection, highlighting the effectiveness of deep neural networks in identifying evolving threats. Their adaptability to changing attack patterns is emphasized, underlining their importance for real-time security monitoring. In parallel, ensemble learning is explored, combining multiple models to improve overall detection accuracy and create a robust defense against a spectrum of cyber threats. The literature reviewed highlights the importance of behavioral analysis, with a novel approach that integrates user behaviour profiling with anomaly detection. This has proven effective in identifying suspicious activity within a network, particularly insider threats and stealthy attacks. Another behavioral framework using User and Entity Behavior Analytics (UEBA) is presented for enhanced anomaly detection, highlighting the importance of context-aware monitoring in improving threat detection accuracy. Collaborative defense mechanisms emerge as a major focus of the research papers reviewed, exploring the potential of sharing threat information between organisations to enhance collective security monitoring. Their findings underscore the importance of a collaborative approach to staying ahead of rapidly evolving cyber threats. Some types of cyber-attacks are also analysed in the context of a security operations centre (SOC) monitoring environment using a security information and event management (SIEM) tool - Splunk. In conclusion, this survey paper synthesizes recent advances in cyber threat detection methods in security monitoring that integrate machine learning, behavioral analysis, and collaborative defense strategies. As cyber threats continue to evolve, these novel methods provide valuable insights for researchers, practitioners, and organisations seeking to strengthen their cybersecurity defenses. This concise overview emphasises the multi-dimensional approach required to secure digital ecosystems, providing a concise yet comprehensive guide to modern cyber threat detection strategies.

Keywords: cyber security; safety and security; machine learning; data security

Free keywords: SIEM and splunk monitoring; security monitoring; machine learning; behavioral analysis; anomaly detection; threat intelligence

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2024

Preliminary JUFO rating: 1