A Survey on The Accuracy of Machine Learning Techniques for Intrusion and Anomaly Detection on Public Data Sets

Tjut Adek, Rizal and Ula, Munirul (2020) A Survey on The Accuracy of Machine Learning Techniques for Intrusion and Anomaly Detection on Public Data Sets. In: 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 16-17 July 2020, Medan.

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Abstract

Machine learning (ML) is growing popularity due to their ability to solve the problem in many areas. In digital world including information security, some intrusion detection systems (IDS) are being upgraded with Machine Learning elements for improving the performance of the system. It is known that is very limited real data set available for information security (IS) research. Therefore, many IS researches relies on the public data set. However public data set have many limitations. The aim of this paper is to analyze the accuracy and performance of the Machine Learning in intrusion detection system and to highlight some recommendation for future research. This study involves an academic papers systematic literature review on intrusion detection related to the application of machine learning methods using public data set. This paper elaborates the used of Machine Learning algorithms in intrusion detection system, highlighting the accuracy and the limitations of the methods for detecting attackers. The goal of this research is to provide an academic base for future research in the adoption of machine learning methods for IDS.

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