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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
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.
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“The C4.5 algorithm [200], the ID3 algorithm [199], and the CART algorithm [55] are among the well-known decision tree algorithms used for intrusion detection.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [55]
In the citing paper
“Though there are various surveys on ML in networking [18,61,82,142,246,339], this survey is purposefully different.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [82]
In the citing paper
“Whereas, Fadlullah et al. [142] and Buczak et al. [82], both state-of-theart surveys, have a specialized treatment of ML to specific problems in networking.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [82]
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Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey
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Adaptive Intrusion Detection in the Networking of Large-Scale LANs With Segmented Federated Learning
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Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks
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Metadata
- Authors
- Buczak, A. L., & Guven, E.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Buczak, Anna L.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2016
- Volume
- 18
- Issue
- 2
- Pages
- 1153-1176
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2342408547
- Semantic Scholar
- 971766088dfaf63fb55e6f0190b14f28f2c98ad0
- Cited by
- 3,332
- References
- 119
- Citations by year
- 2026: 460 · 2025: 395 · 2024: 297 · 2023: 319 · 2022: 403 · 2021: 447 · 2020: 394 · 2019: 314 · 2018: 217 · 2017: 73 · 2016: 11
- FWCI
- 115.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Department of Homeland Security Network Security Deployment Division (HSSA01-13-C-2709)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection, cybersecurity, machine learning, data mining, algorithmic complexity
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