Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
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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
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection.
TL;DR: Kitsune is presented: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner, and demonstrates that Kitsune can be a practical and economic NIDS.
Journal ArticleDOI
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
TL;DR: The Python package tsfresh (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests) accelerates this process by combining 63 time series characterization methods, which by default compute a total of 794 time series features, with feature selection on basis automatically configured hypothesis tests.
Journal ArticleDOI
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
TL;DR: A comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems and presents the opportunities, advantages and shortcomings of each method.
Journal ArticleDOI
Network Intrusion Detection for IoT Security Based on Learning Techniques
TL;DR: This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques and provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems.
Journal ArticleDOI
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
TL;DR: A survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study to evaluate the efficiency of several methods are presented.
References
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