Enhanced Network Anomaly Detection Using Autoencoders: A Deep Learning Approach for Proactive Cybersecurity
Judy Simon, Nellore Kapileswar, Rajamanickam Vani, Narala Madhukara Reddy, Dudekula Moulali, Anki Reddy Narayana Reddy
Abstract
Anomaly detection in network traffic is core part of modern network management. It plays vital role in finding the security threats and performance issues. Finding the threats using the machine learning (ML) method provides an efficient solution to dynamically lean normal network behaviour and detect deviation indicative of anomalies. This study proposed a Support vector machine with Autoencoder for detecting the anomalies in network traffic. Implementing ML methods for threat identification improves network security by enabling rapid response to potential threats and issues. Hence this method also reducing downtime and improving overall network reliability. This study indicates the benefits of ML driven anomaly detection, highlighting its capability to provide a proactive security posture, rapid identification and resolution of anomalies ensuring an efficient network infrastructure.