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IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks.
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- Author(s): Wang J;Wang J; Liu Y; Liu Y; Feng H; Feng H
- Source:
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2022 Jan; Vol. 19 (2), pp. 1280-1303. Date of Electronic Publication: 2021 Dec 02.
- Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
- Language:
English
- Additional Information
- Source:
Publisher: American Institute of Mathematical Sciences;; _b Beihang University Country of Publication: United States NLM ID: 101197794 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1551-0018 (Electronic) Linking ISSN: 15471063 NLM ISO Abbreviation: Math Biosci Eng Subsets: MEDLINE
- Publication Information:
Original Publication: Springfield, MO, USA : [S.l.] : American Institute of Mathematical Sciences; Beihang University
- Subject Terms:
- Abstract:
Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.
- Contributed Indexing:
Keywords: convolutional neural network; detect attacks; distributed denial of service; firefly algorithm; software-defined internet of things
- Publication Date:
Date Created: 20220209 Date Completed: 20220314 Latest Revision: 20220314
- Publication Date:
20240829
- Accession Number:
10.3934/mbe.2022059
- Accession Number:
35135204
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