Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN
- Submitting institution
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University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13118242
- Type
- D - Journal article
- DOI
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10.1016/j.future.2019.10.015
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 763
- Volume
- 111
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes sFlow and adaptive polling-based sampling with intrusion detection and deep learning for efficient detect of Distributed Denial of Service (DDoS) attacks. Such attacks which are considered as one of the most rampant attacks in the modern networking infrastructures. This novel work helps to lower down processing and network overhead of switches in Software Defined Networks (SDN) by deploying sFlow and adaptive polling based sampling. In SDN control-plane, it improves detection accuracy, with collaborative deployment of intrusion detection with Stacked Autoencoders.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -