Strengthening Endpoint Security: Integrating Network Access Control to Protect Enterprise Assets

Abstract

The increasing complexity of organizational networks has resulted in a greater demand for comprehensive endpoint security solutions to safeguard critical assets. The objective of this endeavor is to examine the proactive implementation of Network Access Control (NAC) to enhance endpoint security within an organization. The proposed method employs anomaly detection and machine learning (ML) to autonomously identify and deactivate compromised or illicit devices in real time. Machine learning algorithms like K-means clustering can classify devices by activity. By perpetually analyzing network traffic patterns and identifying any anomalous behaviors, the machine learning approach ensures that only authorized devices can access corporate resources. The results demonstrate significant advancements in endpoint protection and offer a scalable solution to the enterprise network security issue.