Anomaly Detection in Data Centers using Isolation Networks

Abstract

The given research paper proposes a direction or an approach that is novel and efficient for the purpose of detecting anomalies in data centers using isolation networks. Data centers are critical infrastructure in modern society, responsible for hosting and managing large amounts of data and providing computational resources for a variety of applications. As such, it is crucial for making sure that the security is maintained and optimal performance of data centers. The process of detecting anomalies is a key component of this effort, as it can help identify security threats and performance issues. The proposed approach uses isolation networks, a type of unsupervised machine learning algorithm, to identify anomalies in server and network behavior based on input features such as CPU utilization, memory usage, and network traffic. The paper evaluates the performance of the approach using a publicly available dataset of data center metrics, and show that it can achieve high accuracy in identifying anomalies while maintaining a low false positive rate. The paper's results suggest that isolation networks have significant potential for improving the security and performance of data centers, and we discuss several potential avenues for future research in this area. Overall, this paper contributes to the growing body of literature on machine learning for data center management and highlights the importance of anomaly detection in ensuring the reliability and security of these critical infrastructure systems.