Exploring the application of machine learning techniques for anomaly detection in network security
Krishna Kant Dixit, Birendra Kr Saraswat, Vijay Kr Sharma · 2024
In the digital era, network security is a crucial concern as businesses face growing dangers from cyber-attacks. By detecting odd patterns or behaviors in network traffic, anomaly detection is essential in identifying and reducing these dangers. In this area, machine learning techniques have showed promise since they enable the analysis of huge amounts of data and the discovery of minute irregularities that might be signs of an attack. To improve the precision and effectiveness of anomaly detection, the study will look at various data forms, feature engineering strategies, and model architectures. In addition, problems with unbalanced datasets, adversarial assaults, and real-time detection in dynamic network settings will be addressed in this research. The results of this study will aid in the creation of strong, proactive network security solutions, giving businesses the ability to quickly identify and address network abnormalities.