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
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
TL;DR: The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification.
Journal ArticleDOI
Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
Journal ArticleDOI
Survey of intrusion detection systems: techniques, datasets and challenges
TL;DR: A taxonomy of contemporary IDS is presented, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes are presented, and evasion techniques used by attackers to avoid detection are presented.
Journal ArticleDOI
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Raouf Boutaba,Mohammad A. Salahuddin,Noura Limam,Sara Ayoubi,Nashid Shahriar,Felipe Estrada-Solano,Felipe Estrada-Solano,Oscar Mauricio Caicedo +7 more
TL;DR: This survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking, and jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies.
Journal ArticleDOI
Machine Learning and Deep Learning Methods for Cybersecurity
Yang Xin,Lingshuang Kong,Liu Zhi,Yuling Chen,Yanmiao Li,Hongliang Zhu,Mingcheng Gao,Haixia Hou,Chunhua Wang +8 more
TL;DR: This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method.
References
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Journal ArticleDOI
Anomaly-based network intrusion detection: Techniques, systems and challenges
TL;DR: The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.
Journal ArticleDOI
A survey of techniques for internet traffic classification using machine learning
TL;DR: This survey paper looks at emerging research into the application of Machine Learning techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques.
Book ChapterDOI
A tutorial on learning with Bayesian networks
TL;DR: In this article, the authors discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models, including techniques for learning with incomplete data.
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The immune system, adaptation, and machine learning
TL;DR: A dynamical model for the immune system is described that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer, and has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system.
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A theory and methodology of inductive learning
TL;DR: The presented theory views inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements.
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