Insider Threat Classification Using KNN Machine-Learning Technique

The insider threat has grown to be a broadly conventional problem, and it has become the most predominant demanding situation in the field of cybersecurity. This system shows that threats require a unique method of detection, techniques, and various tools that can simplify correct and speedy malicious insider detection. As insiders live at the back of the organizational level and regularly have access to the network, detection, and prevention of insider threats become very complicated. Later, a few issues are brought up based on the findings from the examined work, and new gaps and difficult circumstances are identified. This paper represents an up-todate review of all major machine learning algorithms employed for detecting insider threats. Moreover, various issues involved during the formulation of multiple algorithms for detecting insider threats are discussed.