A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

By Anna L. Buczak, Erhan Guven. IEEE Communications Surveys and Tutorials, 2016

1,128 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/971766088dfaf63fb55e6f0190b14f28f2c98ad0

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Citations in the corpus

A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “This process is repeated until a leaf node is reached which represents the class of the tested instance (Buczak and Guven, 2016).”
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? (2018, methodology): “ML techniques such as SVMs, K-NNs, and NNs have been used for intrusion detection [15].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “More recently, [82] looked at the application of Data Mining and ML for cyber-security intrusion detection.”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “[248] A. L. Buczak and E. Guven, ‘‘A survey of data mining and machine learning methods for cyber security intrusion detection,’’ IEEE Commun.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “In addition, Neural Networks (NNs) can be utilized to detect network intrusion and in this context, authors in [31] have provided a comprehensive review of ML and data mining techniques for intrusion detection in cyber analytic applications.”
Malicious URL Detection using Machine Learning: A Survey (2017, background): “fy the limitations of the current models [162], and provide directions for future research. 7 RELATED SURVEYS Recent years have witnessed innovative applications of machine learning in cyber security [26, 52, 164]. They discuss a variety of other cyber threats, and do not focus on Malicious URL Dete”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “For example, Buczak and Guven (2016) provide two paragraphs for a Random Forest review and cite three works which use this method.”
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey (2019, background): “Several previous surveys [3–5] have classiﬁed research efforts by their applied machine learning algorithms.”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “being used to detect the intrusion attack [88], [147][148].”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “AIDS methods can be categorized into three main groups: Statistics-based (Chao et al., 2015), knowledge-based (Elhag et al., 2015; Can & Sahingoz, 2015), and machine learning-based (Buczak & Guven, 2016; Mesh-ram & Haas, 2017).”