Advancements in Anomaly Detection Techniques in Network Traffic: The Role of Artificial Intelligence and Machine Learning

Abstract

Purpose: This paper examines the most recent techniques for identifying irregularities in network data, with an emphasis upon machine learning (ML) and artificial intelligence (AI). Understanding how these technologies improve anomaly detection and overall network security is the goal of the study.
Design/Methodology/Approach: A thorough examination of scholarly works, business analyses, and conference proceedings from the previous ten years was carried out. The study looks into supervised learning, unsupervised learning, and deep learning, among other AI and ML approaches. In order to evaluate these techniques' efficacy, advantages, and disadvantages in network anomaly detection, a comparative analysis was carried out.
Findings/Results: The analysis shows that the identification of anomalies in network traffic is greatly enhanced by the use of AI and ML approaches. Methods such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have shown to be very successful in recognizing intricate patterns. Nonetheless, issues with data quality, computational complexity, and interpretability of models continue to exist.
Originality/Value: This paper offers a current assessment of machine learning and artificial intelligence applications in network anomaly detection, emphasizing emerging trends and areas for further study. For academics and practitioners looking to improve network security using sophisticated detection methods, it provides insightful information.