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
Evaluation methodology for mission-centric cyber situational awareness capabilities
David Sandoval Rodríguez-Bermejo,Roumen Daton Medenou,Ramis Pasqual de Riquelme,Jorge Maestre Vidal,Francesco Torelli,Salvador Llopis Sanchez +5 more
TL;DR: In this paper, the authors introduce a novel evaluation framework able to guide the evaluation of cyber situational awareness (CSA) related tools, for which three core validation concepts are discussed: software, operational and application tests.
Journal ArticleDOI
An optimal feature based network intrusion detection system using bagging ensemble method for real-time traffic analysis
TL;DR: A novel NIDS using the decision tree-based Bagging ensemble method, where the NSL-KDD dataset has been used for experimental purposes is proposed and exhaustive performance evaluation confirms that the proposed MFO-ENSEMBLE method achieves an 87.43% detection rate and incurs minimal time overhead amongst all classification techniques.
Proceedings ArticleDOI
An Incremental Broad Learning Approach for Semi-Supervised Classification
TL;DR: An Incremental Semi-supervised Broad Learning method (ISBL) based on BLS to classify a partially labeled dataset, which applies manifold regularization to explore the underlying data distribution and improve accuracy.
Journal ArticleDOI
Impact of Industrial Agglomeration on Regional Economy in a Simulated Intelligent Environment Based on Machine Learning
TL;DR: In this article, the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning is introduced, and the relationship between industrial integration and regional economic development is tested empirically.
Book ChapterDOI
Real-Time Distributed Denial-of-Service (DDoS) Attack Detection Using Decision Trees for Server Performance Maintenance
Mrunmayee Khare,Rajvardhan Oak +1 more
TL;DR: This system suggests a lightweight data mining approach to detect DDoS attacks using decision trees which outperforms the existing algorithms in terms of sensitivity and specificity and operates in a live system in real-time conditions.
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.
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