Adaptive Anomaly Detection for Identifying Emerging Cybersecurity Threats in Networked Systems

Abstract

The rapid evolution of cybersecurity threats in networked systems has increased the demand for intelligent, scalable, and adaptive intrusion detection mechanisms within the Computer Science domain, particularly in cybersecurity, machine learning, and network security. This study focused on developing and evaluating an adaptive anomaly detection framework for identifying emerging cybersecurity threats through network traffic analysis, feature extraction, machine learning classification, and concept drift adaptation. The computational framework incorporated network traffic characteristics, including packet frequency, connection duration, traffic volume, communication protocols, and source-destination behavior, to distinguish legitimate activities from anomalous network patterns. Python-based computational techniques were employed for data preprocessing, model development, anomaly classification, and comparative performance evaluation. The analysis examined accuracy, precision, recall, F1-score, false-positive rate, and computational processing time. The reported analysis indicated that the adaptive anomaly detection model achieved 96.80% accuracy, 95.90% precision, 95.40% recall, and a 95.65% F1-score, with a false-positive rate of 3.10%. Furthermore, its detection accuracy remained 89.70% under emerging attack patterns, demonstrating the potential importance of adaptive learning in dynamic network environments. The significance of this study lies in addressing the limitations of conventional signature-based and static machine learning detection approaches by integrating adaptive learning and concept drift management into a unified cybersecurity framework. The proposed approach provides a foundation for improving threat identification, reducing false alarms, and supporting more responsive network security systems.