Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TLDR
The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.Abstract:
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.read more
Citations
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Proceedings ArticleDOI
Machine Learning for Security and Security for Machine Learning: A Literature Review
Nuruddin Wiranda,Fal Sadikin +1 more
TL;DR: A literature survey and review of the topic of machine learning for security and security for machine learning is presented in this article, where solutions to three research questions (RQ) through delivering graphs, charts, and facts to summarize data are presented.
Book ChapterDOI
A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment
TL;DR: The objective of the chapter is to review Machine learning methods that are applied to handle zero-day attacks in a cloud environment.
Book ChapterDOI
Machine Learning-Based Intrusion Detection System with Recursive Feature Elimination
TL;DR: In this paper, three different machine learning approach, namely decision tree, random forest and support vector machine (SVM), were used to train the model and the experiment result shows that proposed IDS performs well as compared to the base model with the accuracy of 99.1.
Journal ArticleDOI
Systems Science of Secure and Resilient Cyberphysical Systems
TL;DR: The authors are in the midst of a pervasive, profound shift in the way humans engineer physical systems and manage their physical environment using networking and IT, and physical systems can now be attacked through cyberspace.
Book ChapterDOI
Taxonomy of Supervised Machine Learning for Intrusion Detection Systems
Ahmed Ahmim,Mohamed Amine Ferrag,Leandros A. Maglaras,Makhlouf Derdour,Helge Janicke,George Drivas +5 more
TL;DR: This paper presents a taxonomy of supervised machine learning techniques for intrusion detection systems (IDSs) and analysis and comparison of each IDS along with their pros and cons are provided.
References
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Related Papers (5)
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more