UDP Flooding Attack Detection Using Information Metric Measure
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Many studies classify feature selection methods into three categories: filter-based techniques, wrapper techniques, and embed techniques.
“Many studies classify feature selection methods into three categories: filter-based techniques, wrapper techniques, and embed techniques”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [52]
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
On Measures of Entropy and Information
Rényi, A. (1961) · Project Euclid (Cornell University) · Cited by 4,031 · see citing works
Collaborative Detection of DDoS Attacks over Multiple Network Domains
Chen, Y., Hwang, K., & Ku, W. (2007) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2007.1111 · Cited by 281 · see citing works
Analysis of UDP Traffic Usage on Internet Backbone Links
Zhang, M., Dusi, M., John, W., et al. (2009) · International Symposium on Applications and the Internet (SAINT) · doi:10.1109/saint.2009.65 · Cited by 61 · see citing works
Proactive Surge Protection: A Defense Mechanism for Bandwidth-Based Attacks
Chou, J., Lin, B., Sen, S. B., et al. (2009) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2009.2017199 · Cited by 42 · see citing works
DDoS Defense Mechanisms: A New Taxonomy
Keshariya, A., & Foukia, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11207-2_17 · Cited by 24 · see citing works
Use of IP Addresses for High Rate Flooding Attack Detection
Ahmed, E., Mohay, G., Tickle, A. B., et al. (2010) · IFIP advances in information and communication technology · doi:10.1007/978-3-642-15257-3_12 · Cited by 22 · see citing works
Flooding attacks detection in traffic of backbone networks
Salem, O., Makke, A., Tajer, J., et al. (2011) · Conference on Local Computer Networks · doi:10.1109/lcn.2011.6115504 · Cited by 17 · see citing works
Distributed Denial of Service Attacks Detection Method Based on Conditional Random Fields
Chen, S., Wu, J., Xiao-long, Y. E., et al. (2013) · Journal of Networks · doi:10.4304/jnw.8.4.858-865 · Cited by 13 · see citing works
Classification of UDP traffic for DDoS detection
Bardas, A. G., Zomlot, L., Sundaramurthy, S. C., et al. (2012) · Cited by 12 · see citing works
Autonomous Agent for DDoS Attack Detection and Defense in an Experimental Testbed
Preetha, G., Devi, B. S. K., & Shalinie, S. M. (2014) · International Journal of Fuzzy Systems · Cited by 12 · see citing works
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Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works
- Authors
- Boro, D., Basumatary, H., Goswami, T., & Bhattacharyya, D. K.
- Affiliations
- Tezpur University (IN)
- Corresponding author
- Boro, Debojit
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Singapore
- Published
- 2016
- Pages
- 143-153
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W2291413706
- Cited by
- 10
- References
- 11
- Citations by year
- 2025: 1 · 2023: 2 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 2 · 2016: 1
- FWCI
- 3.92
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- UDP flooding, DDoS attack detection, generalized entropy, information metrics, network traffic analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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