Automation of Endpoint Security Using Machine Learning

Automation of Endpoint Security Using Machine Learning

연구 분야: Safety

학회: International Conference on Advancements in Smart Computing and Information Security

With the increasing usage of the internet and technology, cyber threats are also increasing which target endpoint devices. This work focuses on the importance of securing the endpoints and the highlights the role of Intrusion Detection Systems (IDS) in mitigation of cyber threats. This research work provides insight into various threats related to the Network environment and analysis of intrusions. To mitigate such threats, we propose customized IDS mechanisms for secured endpoints using different ML techniques with the NIDS dataset. The patterns of anomaly are identified which leads to proactive threat detection. By continuously updating predefined rules based on emerging threats, automated Endpoint Security mechanisms can be deployed to mitigate security threats effectively. The results suggest some models perform well to detect threats.