Insider Threats in Cybersecurity: Detection and Mitigation Strategies

Karri, Krishna Reddy (2024) Insider Threats in Cybersecurity: Detection and Mitigation Strategies. Masters thesis, Dublin, National College of Ireland.

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Abstract

Insider threats have become one of the most common and dangerous trends in the field of cybersecurity. Nowadays, with the World Wide Web and spread of insecure technologies bring critical threats for information security, its confidentiality and stability of organizational performances. Insider threats are from insiders – employees / contractors / anyone with legitimate access to sensitive systems and their malicious activities are intentional or accidental. Insider threat is at the core of this research and the investigation made in this study aims to promote improved detection and prevention of insider threats through technological solutions such as behavioural assessment, machine learning, and policy frameworks. Behavioural analysis can facilitate an ongoing analysis of user activities compared to norms and do it in real time thus providing an additional layer of protection. Supervised and unsupervised learning algorithms together with the big data analysis effectively creep through different types and sizes of data to detect both, recognized and new threats. Furthermore, real-world policies are also evaluated in terms of their ability to facilitate security-oriented cultures where human factors have been adequately discussed. Samples of activities used in the research include the use of affiliated tools such as Splunk, TensorFlow and data from the CERT Insider Threat Center and insider threat simulation scenarios. Evaluation measures include the detector accuracy as per the confusion matrix of correct negative, correct positive, false negatives, and false positives as well as, the response time of the system in case of an invasion attempt as expressed by ROC curves. The study will provide evidence of increased rates of accurate detection of insider threats, fewer numbers of false alarms and increased response effectiveness in putting an end to insiders’ unauthorized behaviour; all of which will provide a massive boost to organizational defences against insider threats.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Khadija UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	23 Jul 2025 14:13
Last Modified:	23 Jul 2025 14:13
URI:	https://norma.ncirl.ie/id/eprint/8218

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