Security Enhancement of Contactless Tachometer-Based Cyber Physical System

Saraf, Kundan Kumar Rameshwar; Malathi, P.; Shaw, Kailash

doi:10.1007/978-981-16-0935-0_8

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Cyber-attacks on cyber-physical systems (CPS) will be detected using Splunk Enterprise Security software (Saraf, 2020), and predictive analysis of CPS cyber-attacks will be performed using Splunk (Saraf, 2022). Securing a contactless tachometer-based brushless DC motor involves a combination of lightweight cryptographic algorithms and Splunk (Saraf, 2021).…”

Section: Security Attacks and Splunk Monitoringmentioning

confidence: 99%

Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring

Shelke,

Hamalainen

2024

eccws

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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.

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Section: Security Attacks and Splunk Monitoringmentioning

confidence: 99%