AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection - Priyanshi Nahar (International Journal of Computer Science Languages, 2025)
The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly detection, critically evaluating corresponding challenges like data quality, computational complexity, and interoperability of the model. As illustrated through methods like CNNs and RNNs successfully identifying complex patterns, AI and ML provide improved functionalities in anomaly detection. Yet, concerns like the need for high-quality labeled data (for supervised learning) and difficulty in parameter tuning (for unsupervised learning) persist. Deep learning techniques are associated with challenges of computational expense and interoperability. This study presents a state-of-the-art evaluation of AI and ML in the area, outlining upcoming trends. It suggests future research avenues for optimizing model structures, enhancing interoperability and solving scalability problems to transcend current limitations and improve the efficiency of network anomaly detection systems. This review provides beneficial information for practitioners and scholars working to enhance network security using advanced detection techniques.
Keywords: anomaly detection; ensemble techniques; feature engineering; integration complexities; performance quantification