Detecting Insider Threats in Cybersecurity Using Machine Learning and Deep Learning Techniques

The subject of cybersecurity is evolving at a rapid pace, and one of the challenges that organisations face is keeping up with the ever-evolving nature of the threat posed by their own employees. These risks, which come from contractors or staff inside the organisation, usually get by the normal security measures. This is due to the fact that these individuals are trusted folks. This research presents a comprehensive approach to the identification of insider hazards by making use of both machine learning and deep learning methodologies. We propose a hybrid model that combines deep neural networks, which are able to capture fine-grained behavioural subtleties, with featureengineered patterns that are indicative of anomalous insider behaviours. This model would be used to identify anomalous insider behaviours. Our model had a detection accuracy of 96.3%, which was higher than the existing state-of-the-art approaches, and it surpassed them by using a dataset that was created from user activity records and system logs from multiple different companies. In addition, the deep learning component made it feasible to identify minute patterns of potentially dangerous behaviour that had previously escaped detection. When traditional machine learning techniques are coupled with deep learning methods, as shown by our findings, it may be possible to develop insider threat detection technologies that are more effective, dependable, and versatile within the area of cybersecurity.