CYBER HACKING BREACHES PREDICTION AND DETECTION USING MACHINE LEARNING

Abstract

Cyber security threats are growing in frequency and sophistication, making timely prediction and detection of hacking breaches essential for protecting digital assets. This paper presents a machine learning–driven framework for predicting and detecting cyber hacking breaches by combining feature-rich telemetry preprocessing, supervised learning for breach prediction, and anomaly detection for rapid identification of novel intrusions. Raw data sources — including network flows, system logs, authentication events, and endpoint telemetry — are normalized, enriched with contextual features (e.g., time-of-day, geolocation, userbehaviour baselines), and encoded to handle high dimensionality and class imbalance. For breach prediction we evaluate tree-based ensembles and gradient-boosting models with temporal windowing to forecast highrisk hosts or accounts within a short horizon. For detection we implement unsupervised and semi-supervised techniques (auto-encoders, isolation forests, and one-class SVM variants) to surface deviations from learned normal behavior. The framework emphasizes explainability (feature importance and local explanations) and operational constraints (low latency, incremental learning, and false-positive control). Experiments on mixed real-world and benchmark datasets show the approach substantially improves early-warning recall while reducing false alarms compared to baseline rule-based systems. The proposed system offers a practical, extensible path for security teams to prioritize responses and harden defenses against emerging hacking campaigns.