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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Journal ArticleDOI
Cybersecurity challenges in energy sector (virtual power plants) - can edge computing principles be applied to enhance security?
TL;DR: In this article, the authors present a comprehensive edge-based security architecture to help reduce the risks and help secure the physical systems and ensure privacy and data protection in the energy sector.
Book ChapterDOI
Cyber Operational Planning
TL;DR: The process of collecting business, software, or information systems’ requirements in general and security requirements in particular can take different approaches, structured or unstructured.
Book
Machine Learning for Cyber Physical Systems
TL;DR: Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control and Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases.
Journal ArticleDOI
Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach
TL;DR: In this paper, the authors investigated username enumeration attack detection on SSH protocol by using machine-learning classifiers, including k-nearest neighbor (KNN), naive Bayes (NB), random forest (RF), and DT.
Proceedings ArticleDOI
A Method Based on Hierarchical Spatiotemporal Features for Trojan Traffic Detection
TL;DR: A neural network detection model (HSTF-Model) based on hierarchical spatiotemporal features of traffic is proposed that combines deep learning algorithms with expert knowledge through feature encoders and statistical characteristics to improve the self-learning ability of the model.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Related Papers (5)
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more