AI-Based Approaches For Network Anomaly Detection

Network anomaly detection has become a critical component of modern cybersecurity, driven by the increasing complexity and scale of network infrastructures. Traditional rule-based and signature-based detection methods are often insufficient to identify sophisticated and evolving cyber threats. This study explores AI-based approaches for network anomaly detection, emphasizing the use of machine learning (ML) and deep learning (DL) techniques to identify unusual patterns and behaviors in network traffic. It examines various models such as supervised, unsupervised, and semi-supervised learning, along with advanced techniques including neural networks, clustering algorithms, and autoencoders. The paper also highlights the role of real-time data processing, feature engineering, and big data analytics in enhancing detection accuracy and responsiveness. Applications across sectors such as healthcare, finance, and cloud computing are discussed to demonstrate the effectiveness of AI-driven anomaly detection systems. Furthermore, the study addresses key challenges including high false positive rates, data imbalance, scalability, and privacy concerns, and proposes solutions such as hybrid models, adaptive learning, and explainable AI. The findings suggest that AI-based approaches significantly improve the efficiency, accuracy, and adaptability of network anomaly detection systems in dynamic and distributed environments.