Detecting Insider Threats in Cybersecurity Using Machine Learning

Threats in cyber security increasing now a days. In order to detect insider threats in cyber security environments, this research investigates the creation of a Network Intrusion Detection System (NIDS) that makes use of machine learning techniques. In order to identify suspicious activity and unauthorized access, the suggested system examines network traffic. The NIDS minimizes false positives and achieves high accuracy by using ensemble models, particularly Decision Trees and Logistic Regression, which are optimized by Grid Search. The simulation findings show that, in comparison to independent methods, integrating models produces a more robust and dependable detection mechanism. From Denial-of-Service (DoS) attacks to increasingly sophisticated insider threats, the NIDS continuously demonstrated a high detection accuracy while maintaining a controllable false positive rate. This feature emphasizes how useful machine learning models are for improving NIDS's responsiveness and flexibility. The system's appropriateness for deployment in expansive, dynamic network environments is demonstrated by simulation results, which validate its capacity to expand and sustain performance even in the face of high network traffic. The technique, simulation results, and future directions-such as real-time integration and improved scalability for bigger networks-are covered in the research.