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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Posted Content
A Hybrid Intrusion Detection with Decision Tree for Feature Selection.
TL;DR: A wrapper-based hybrid intrusion detection modeling with a decision tree algorithm to guide the selection process is proposed and shows that it takes high computational time in comparison to the filter-based methods whilst achieves similar results.
Journal ArticleDOI
Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
TL;DR: In this article, the authors focused on the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks, which occurs when one class heavily outnumbers examples from the other classes.
Book ChapterDOI
Review on Cyber Security Intrusion Detection: Using Methods of Machine Learning and Data Mining
TL;DR: This review contains information about how Intrusion detection methods in cyber security can be used with Machine Learning and Data Mining techniques.
Journal ArticleDOI
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring
TL;DR: UN-AVOIDS as mentioned in this paper is an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to [0, 1]), rather than hard binary-decision.
Journal ArticleDOI
A Robust Cybersecurity Solution Platform Architecture for Digital Instrumentation and Control Systems in Nuclear Power Facilities
TL;DR: This paper presents the proposed cybersecurity architecture and demonstrates its efficacy with a simulated cyberattack on a cyber-physical system testbed, providing a solution for prevention, detection, and response to cyberattacks that is congruous with the defense-in-depth strategies of other NPP safety and security systems.
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