MACHINE LEARNING-BASED IDENTITY PATTERN ANALYSIS FOR INTELLIGENT CYBERSECURITY THREAT DETECTION

Abstract

The increasing sophistication of cyber threats has created a need for intelligent security systems capable of identifying suspicious user activities, unauthorized access, compromised accounts, and insider threats. Traditional cybersecurity mechanisms that rely primarily on predefined rules and known attack signatures may struggle to detect previously unseen threats and subtle deviations in user behaviour. Machine learning provides opportunities to analyse identity-related activity patterns and distinguish normal behaviour from potentially malicious actions. This research proposes a machine learning-based framework for identity pattern analysis and intelligent cybersecurity threat detection. The framework examines authentication records, login frequency, access histories, device information, session characteristics, and deviations from established user behaviour. Data preprocessing and feature engineering are used to prepare identity-related records for analysis, while supervised and unsupervised learning techniques are considered for threat classification and anomaly detection. Candidate algorithms include Random Forest, Support Vector Machine, Isolation Forest, and Long Short-Term Memory networks for sequential behavioural data. The proposed framework will be evaluated using accuracy, precision, recall, F1- score, false-positive rate, and computational efficiency. Explainability and privacy considerations will also be incorporated to support responsible security decisions. The study aims to develop an adaptive approach to identity-based threat detection that can assist organizations in identifying suspicious activities and strengthening access security. The effectiveness of the framework must be established through empirical experimentation using appropriate cybersecurity datasets.