<![CDATA[The escalating sophistication of cyber threats necessitates advanced anomaly detection techniques that transcend traditional signature-based methods. This paper presents an automated cybersecurity framework leveraging the Isolation Forest algorithm for unsupervised anomaly detection in network traffic. Using the NSL-KDD dataset, we demonstrate that Isolation Forest achieves 95.2% detection accuracy with a 4.7% false-positive rate, outperforming conventional methods such as One-Class SVM (88.1% accuracy) and Local Outlier Factor (82.3% accuracy) in both computational efficiency and precision. Key advantages include: (1) real-time processing capability (8.2s training time, 4× faster than density-based approaches), (2) effective identification of rare attack types (U2R/R2L), and (3) elimination of dependency on labeled training data. The proposed system integrates dynamic threshold tuning and SHAP-based feature weighting to enhance detection stability and reduce false alarms. The results validate Isolation Forest as a scalable and reliable solution for modern intrusion detection systems, with strong implications for SIEM integration and real-time cybersecurity automation. Challenges in parameter tuning and encrypted traffic analysis are discussed, alongside future directions involving hybrid deep learning architectures.]]>
Copyrights © 2025
