Cited by 3,332Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

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

Buczak, Anna L., Guven, Erhan·2016·IEEE Communications Surveys & Tutorials

DOI 10.1109/comst.2015.2494502

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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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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Fuzzy sets
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Particle swarm optimization
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Fuzzy sets
Zadeh, L. A. (1996) · Cited by 46,960
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Untitled work
(1992) · Cited by 23,431

Show all 119 references

Research works printed but not linked: 14

Shearer, 2000, The CRISP-DM model: The new blueprint for data mining, J. Data Warehouse., 5, 13
2000, R Core Team
Jacobson, 1989, The Tcpdump Manual Page
Combs, 2014, Wireshark
2014, Sourcefire
Stolfo, 2014, KDD Cup 1999 Data Set
2015, IBM
10.4108/wkdd.2008.2686 · DOI
2014, Politecnico di Torino, Analyzer 3.0
Baralis, 2008, Generalized Itemset Discovery by Means of Opportunistic Aggregation
Markov, 1971, Extension of the limit theorems of probability theory to a sum of variables connected in a chain, Dynamic Probabilistic Systems, 1
10.2307/2984875 · DOI
Brauckhoff, 2008, Flame: A low-level anomaly modeling engine
Paxson, 2004, Bro 0.9

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BackgroundMethod
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“…G ( φ Δ ) is large enough and the depth of the tree is less than the maximum value, recurse the left and right subsets Q I ( φ Δ ) and Q r ( φ Δ ) [35, 36]. method part of this paper uses the above method to study the data mining method of diabetes risk based on electronic medical record…”
Method
Retracted
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection
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Blockchain-based synergistic solution to current cybersecurity frameworks
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“Another research paper provides the literature survey of data mining and machine learning techniques for cybersecurity intrusion detection [7].”
Background
A Review on Machine Learning based Security Approaches in Intrusion Detection System
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Authors: Buczak, A. L., & Guven, E.
Affiliations: Johns Hopkins University (US)
ORCID: Buczak, Anna L.
Venue: IEEE Communications Surveys & Tutorials
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2016
Volume: 18
Issue: 2
Pages: 1153-1176
ISSN: 1553-877X (ISSN-L)
2373-745X
DOI: 10.1109/comst.2015.2494502
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
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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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

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