NEURAL NETWORK-BASED INSIDER THREAT DETECTION FOR ENHANCED CYBERSECURITY

Abstract

Insider threats have become one of the most critical challenges in modern cybersecurity, as malicious or negligent insiders can exploit their authorized access to compromise sensitive organizational data and systems. Traditional security mechanisms such as rule-based monitoring and signature-based detection are often ineffective in identifying insider attacks because these threats originate from trusted users and may follow normal usage patterns. To address this challenge, advanced artificial intelligence techniques, particularly neural networks, have been explored for detecting anomalous behaviors and potential insider threats in real time.This study presents a Neural Network-Based Insider Threat Detection System designed to enhance cybersecurity by analyzing user behavior patterns within organizational networks. The proposed system utilizes machine learning models to monitor user activities such as login patterns, file access behavior, system usage, and network interactions. By learning normal user behavior, the neural network can identify deviations that may indicate malicious insider activities, including data theft, unauthorized access, or privilege misuse.The system employs data preprocessing, feature extraction, and neural network-based classification to accurately detect suspicious activities. Experimental evaluation demonstrates that the proposed approach improves detection accuracy and reduces false alarms compared to traditional security mechanisms. This intelligent system provides organizations with a proactive solution for protecting sensitive information and strengthening cybersecurity infrastructure.