Anomaly Detection in Big Data using Machine Learning

What is it about?

Industrial Information Technology (IT) infrastructures are often vulnerable to cyberattacks. To ensure security to the computer systems in an industrial environment, it is required to build effective intrusion detection systems to monitor the cyber-physical systems (e.g., computer networks) in the industry for malicious activities. This paper aims to build an intrusion detection system via leveraging Machine Learning to protect computer networks from cyberattacks. Based on our empirical evaluation in different use-cases for anomaly detection in real-world network traffic data, we observe that our proposed system is effective to detect anomalies in big data scenarios.

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Photo by Chris Ried on Unsplash

Photo by Chris Ried on Unsplash

Why is it important?

To provide security to cyber-physical systems, this paper is helpful. Also, the proposed model is very useful for industrial big data scenarios.

Perspectives