INTELLIGENT INSIDER THREAT DETECTION USING USER BEHAVIOR ANALYSIS

Abstract

Insider threats represent one of the most critical cybersecurity challenges faced by modern organizations. Unlike external cyberattacks, insider threats originate from legitimate users who possess authorized access to sensitive organizational resources. These threats may arise due to malicious intent, negligence, or compromised credentials. Traditional security mechanisms such as firewalls and intrusion detection systems primarily focus on external attacks and often fail to detect abnormal behavior from trusted users within the organization. Consequently, organizations require intelligent solutions capable of continuously monitoring user activities and identifying anomalous behavioral patterns.

This research proposes an Intelligent Insider Threat Detection System using User Behavior Analysis (UBA) combined with machine learning techniques. The proposed framework analyzes behavioral patterns derived from system logs, email communication records, file access activities, and login patterns to identify suspicious insider activities. The system employs data preprocessing, feature extraction, and anomaly detection techniques to model normal user behavior and detect deviations indicating potential threats. The model is trained and evaluated using the CERT Insider Threat Dataset, which contains realistic simulated insider attack scenarios including data exfiltration, privilege misuse, and sabotage.

Machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks are used to identify abnormal behavioral patterns. Experimental results demonstrate that the proposed system significantly improves detection accuracy compared to traditional rule-based security systems. By providing early detection of insider threats, the proposed approach enhances organizational security, reduces financial losses, and protects sensitive data assets.