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
Mining fuzzy association rules in databases
TL;DR: This paper introduces the fuzzy association rules of the form, 'If X is A then Y is B', to deal with quantitative attributes, using the fuzzy set concept, to find association rules more understandable to human.
Proceedings ArticleDOI
Naive Bayes vs decision trees in intrusion detection systems
TL;DR: It is shown that even if having a simple structure, naive Bayes provide very competitive results, and the good performance of Bayes nets with respect to existing best results performed on KDD'99.
Journal ArticleDOI
An Overview of IP Flow-Based Intrusion Detection
TL;DR: The paper provides a classification of attacks and defense techniques and shows how flow-based techniques can be used to detect scans, worms, Botnets and (DoS) attacks.
Artificial Neural Networks for Misuse Detection
TL;DR: This work presents an approach to the process of misuse detection that utilizes the analytical strengths of neural networks, and the results from the preliminary analysis of this approach are provided.
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Nmap Network Scanning: The Official Nmap Project Guide to Network Discovery and Security Scanning
TL;DR: Nmap Network Scanning is the official guide to the Nmap Security Scanner, a free and open source utility used by millions of people for network discovery, administration, and security auditing.
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