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
Cyber Intrusion Prediction and Taxonomy System Using Deep Learning And Distributed Big Data Processing
TL;DR: This paper builds a prediction model for all attacks together using deep learning with the smallest number of features and optimize the model to achieve the highest accuracy, and develops a model that can accurately predict the threat and the type of attack.
Journal ArticleDOI
Representation learning-based network intrusion detection system by capturing explicit and implicit feature interactions
TL;DR: Li et al. as discussed by the authors proposed RL-NIDS, which consists of two main modules, i.e., unsupervised Feature Value Representation Learning module (FVRL) which aims to learn the feature interactions among categorical features explicitly, and supervised Neural Network for object representation learning (NNRL), which aims at learning the implicit interactions in the representation space.
Journal ArticleDOI
Applying Event Stream Processing to Network Online Failure Prediction
TL;DR: This article describes an OFP system built over Apache Spark that takes a repository of network management events, trains a Random Forest model, and uses this model to predict the appearance of future events in near real time.
Journal ArticleDOI
Scope of machine learning applications for addressing the challenges in next-generation wireless networks
Raj Kumar Samanta,Bikash Sadhukhan,Hiranmay Samaddar,Suvobrata Sarkar,Chandan Koner,Monidepa Ghosh +5 more
TL;DR: This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.
Journal ArticleDOI
Cyber intrusion detection through association rule mining on multi-source logs
TL;DR: In this method, a rule base is constructed to detect cyber intrusion, and an adaptive approach is used to speed up the calculation of the association rule mining, in which the decision depends on the time complexity of the algorithm.
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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