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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Tahaei, H., Afifi, F., Asemi, A., Zaki, F., & Anuar, N. B. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102538 · Cited by 278
Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes
Ferrag, M. A., Maglaras, L. A., Argyriou, A., Kosmanos, D., & Janicke, H. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.017 · Cited by 277
“section III-B, data mining and machine learning methods are used to help discover, determine, and identify unauthorized use and destruction of information systems, such as 4G and 5G cellular networks [246]. Buczak 19 and Guven [246] have found that the most intrusion detection systems used the DARPA 1998, DARPA 1999,…”
Method
A Machine Learning Security Framework for Iot Systems
Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020) · IEEE Access · doi:10.1109/access.2020.2996214 · Cited by 271
Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey
Shen, M., Ye, K., Liu, X., Zhu, L., Kang, J., Yu, S., Li, Q. L., & Xu, K. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208196 · Cited by 306
Cybersecurity, Data Privacy and Blockchain: A Review
Wylde, V., Rawindaran, N., Lawrence, J., et al. (2022) · SN Computer Science · doi:10.1007/s42979-022-01020-4 · Cited by 266
Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment
Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A. U., & Saba, T. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01175-4 · Cited by 333
Machine learning in cybersecurity: a comprehensive survey
Dasgupta, D., Akhtar, Z., & Sen, S. (2020) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/1548512920951275 · Cited by 262
An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks
Sahoo, K. S., Tripathy, B. K., Naik, K., Ramasubbareddy, S., Balusamy, B., Khari, M., & Burgos, D. (2020) · IEEE Access · doi:10.1109/access.2020.3009733 · Cited by 261
A review of Machine Learning-based zero-day attack detection: Challenges and future directions
Guo, Y. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.11.001 · Cited by 319
“While multiple surveys have been conducted on intrusion detection and malware detection using various approaches [14–18], none has been focused on ML-based zero-day attack detection schemes.”
Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
A survey on evolutionary machine learning
Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., Tran, B., Xue, B., & Zhang, M. (2019) · Journal of the Royal Society of New Zealand · doi:10.1080/03036758.2019.1609052 · Cited by 263
“…predict natural disaster, especially earthquake (Asim et al. 2018; Scoble et al. 2013), and other real-world problems, such as web service composition (da Silva et al. 2016), cloud computing (Guzek et al. 2015), cyber security (Buczak and Guven 2016), and video games (Yannakakis and Togelius 2018).”
Background
Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System
Thakkar, A., & Lohiya, R. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.09.026 · Cited by 258
A survey on graph neural networks for intrusion detection systems: Methods, trends and challenges
Zhong, M., Lin, M., Zhang, C., & Xu, Z. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103821 · Cited by 300
Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
Ferrag, M. A., Shu, L., Hamouda, D., & Choo, K. R. (2021) · Electronics · doi:10.3390/electronics10111257 · Cited by 277
Survey on Explainable AI: From Approaches, Limitations and Applications Aspects
Yang, W., Wei, Y.-C., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., et al. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00038-y · Cited by 303
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
Neupane, S., Ables, J., Anderson, W. S., Mittal, S., Rahimi, S., Banicescu, I., & Seale, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3216617 · Cited by 293
Flow-based network traffic generation using Generative Adversarial Networks
Ring, M., Schlör, D., Landes, D., & Andreas Hotho, A. (2018) · Computers & Security · doi:10.1016/j.cose.2018.12.012 · Cited by 261
“Recently, Buczak and Guven [1] presented an overview of the community eﬀort with regard to this issue.”
Background
AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks
Li, J., Zhao, Z., Li, R., & Zhang, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2883344 · Cited by 245

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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