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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Journal ArticleDOI
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
TL;DR: In this article, the authors review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning and investigate their employment in the compelling applications of wireless networks, including heterogeneous networks, cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on.
Journal ArticleDOI
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Hongyu Liu,Bo Lang +1 more
TL;DR: A taxonomy of IDS is proposed that takes data objects as the main dimension to classify and summarize machine learning- based and deep learning-based IDS literature, and believes that this type of taxonomy framework is fit for cyber security researchers.
Journal ArticleDOI
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
William G. Hatcher,Wei Yu +1 more
TL;DR: A thorough investigation of deep learning in its applications and mechanisms is sought, as a categorical collection of state of the art in deep learning research, to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems.
Journal ArticleDOI
Machine Learning in IoT Security: Current Solutions and Future Challenges
TL;DR: This paper systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks, and sheds light on the gaps in these security solutions that call for ML and DL approaches.
Journal ArticleDOI
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection
TL;DR: This paper proposes a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks.
References
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