Intrusion detection by machine learning: A review
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Previous surveys [161,447] cover the literature between 2000-2008, leaving out a decade of work.
“However, [161, 447] cover the literature between 2000-2008, leaving out a decade of work.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [447]
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- Authors
- Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y.
- Affiliations
- National Central University (TW)
- National Sun Yat-sen University (TW)
- National Chung Cheng University (TW)
- Corresponding author
- Lin, Chia-Ying
- Lin, Wei-Yang
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 36
- Issue
- 10
- Pages
- 11994-12000
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2101109743
- Cited by
- 969
- References
- 74
- Citations by year
- 2026: 28 · 2025: 46 · 2024: 67 · 2023: 52 · 2022: 90 · 2021: 91 · 2020: 100 · 2019: 96 · 2018: 68 · 2017: 74 · 2016: 51 · 2015: 58 · 2014: 47 · 2013: 33 · 2012: 31
- FWCI
- 29.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- 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 › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- intrusion detection, intrusion detection systems, network security, machine learning classifiers, ensemble classifiers, hybrid classifier, network attack detection, benchmark dataset
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