The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods
Abstract
Since the first introduction of anomaly-based intrusion detection to the research community in 1987, the field has grown tremendously. A variety of methods and techniques introducing new capabilities in detecting novel attacks were developed. Most of these techniques report a high detection rate of 98% at the low false alarm rate of 1%. In spite of the anomaly-based approach's appeal, the industry generally favors signature-based detection for mainstream implementation of intrusion-detection systems. While a variety of anomaly-detection techniques have been proposed, adequate comparison of these methods' strengths and limitations that can lead to potential commercial application is difficult. Since the validity of experimental research in academic computer science, in general, is questionable, it is plausible to assume that research in anomaly detection shares the above problem. The concerns about the validity of these methods may partially explain why anomaly-based intrusion-detection methods are not adopted by industry. To investigate this issue, we review the current state of the experimental practice in the area of anomaly-based intrusion detection and survey 276 studies in this area published during the period of 2000-2008. We summarize our observations and identify the common pitfalls among surveyed works.
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Through taxonomic research and development, defence systems have employed techniques that analyse relationships between application behaviour and response.
“Through taxonomic research and development, defence systems have employed techniques that analyse relationships between application behaviour and response (sandboxing [Blasing et al. 2010; Greamo and A.Ghosh 2011], dynamic anomaly based scanning [Tavallaee et al. 2010] etc.).”
From A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks (2016) · cites this paper as Tavallaee et al. 2010
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Cites
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An empirical comparison of botnet detection methods
García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
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A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
Heartfield, R., & Loukas, G. (2016) · ACM Computing Surveys · doi:10.1145/2835375 · Cited by 202
Decision Tree Based Intrusion Detection System for NSL-KDD Dataset
Ingre, B., Yadav, A., & Soni, A. K. (2018) · Smart Innovation, Systems and Technologies · doi:10.1007/978-3-319-63645-0_23 · Cited by 150
Explaining Outliers by Subspace Separability
Micenkova, B., Ng, R. T., Dang, X.-H., & Assent, I. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.132 · Cited by 104
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295
Kus, D., Wagner, E., Pennekamp, J., Wolsing, K., Fink, I. B., Dahlmanns, M., Wehrle, K., & Henze, M. (2022) · Proceedings of the 8th ACM on Cyber-Physical System Security Workshop · doi:10.1145/3494107.3522773 · Cited by 28
Metadata
- Authors
- Tavallaee, M., Stakhanova, N., & Ghorbani, A. A.
- Affiliations
- University of New Brunswick (CA)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
- Published
- 2010-09
- Volume
- 40
- Issue
- 5
- Pages
- 516-524
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2103414007
- Cited by
- 280
- References
- 1
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 23 · 2023: 21 · 2022: 34 · 2021: 28 · 2020: 28 · 2019: 22 · 2018: 14 · 2017: 10 · 2016: 14 · 2015: 14 · 2014: 19 · 2013: 9 · 2012: 16
- FWCI
- 13.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- anomaly-based intrusion detection, intrusion detection evaluation, experimental validity, novel attack detection, signature-based detection
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