Labelled Dataset on Distributed Denial‐of‐Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6) Article Swipe
Selvakumar Manickam
,
Adnan Hasan Bdair AIghuraibawi
,
Rosni Abdullah
,
Zaid Abdi Alkareem Alyasseri
,
Karrar Hameed Abdulkareem
,
Mazin Abed Mohammed
,
Ayman Al-Ani
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8060333
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8060333
The most dangerous attack against IPv6 networks today is a distributed denial‐of‐service (DDoS) attack using Internet Control Message Protocol version 6 (ICMPv6) messages. Many ICMPv6‐DDoS attack detection mechanisms rely on self‐created datasets because very few suitable ICMPv6‐DDoS attack datasets are publicly available due to privacy and security concerns. When implemented in a real network, however, a detection system that relies on a dataset with incorrect packet or flow representation and contains unqualified features generates a large number of false alerts. The goal of this work is to create a comprehensive ICMPv6‐DDoS attack dataset that can be used for tuning, benchmarking, and evaluating any detection systems designed to detect ICMPv6‐DDoS attacks. The proposed datasets met the criteria for a good dataset, ensuring their usefulness to other researchers. A GNS3 network simulation tool is used to simulate an IPv6 network and generate ICMPv6 traffic for the dataset. The generated traffic contains both normal and abnormal ICMPv6 traffic, with the abnormal traffic containing ten different ICMPv6‐DDoS attacks based on RA and NS message flooding. Five classifiers were chosen, varying in terms of type, classification performance, and the number of features used, and the results were as follows: decision tree 80%, support vector machine 78%, naïve Bayes 80%, k ‐nearest neighbours 81%, and neural networks 81%. The proposed dataset has been shown to accurately represent attack traffic in tests, with a high detection accuracy and a low false‐positive rate.
Related Topics
Concepts
Denial-of-service attack
Computer science
Trinoo
Application layer DDoS attack
Flooding (psychology)
Computer network
The Internet
Protocol (science)
Network packet
Naive Bayes classifier
Data mining
Computer security
Machine learning
Support vector machine
World Wide Web
Pathology
Psychotherapist
Medicine
Psychology
Alternative medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/8060333
- https://downloads.hindawi.com/journals/wcmc/2022/8060333.pdf
- OA Status
- hybrid
- Cited By
- 18
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224046934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4224046934Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/8060333Digital Object Identifier
- Title
-
Labelled Dataset on Distributed Denial‐of‐Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Selvakumar Manickam, Adnan Hasan Bdair AIghuraibawi, Rosni Abdullah, Zaid Abdi Alkareem Alyasseri, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Ayman Al-AniList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/8060333Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/wcmc/2022/8060333.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/wcmc/2022/8060333.pdfDirect OA link when available
- Concepts
-
Denial-of-service attack, Computer science, Trinoo, Application layer DDoS attack, Flooding (psychology), Computer network, The Internet, Protocol (science), Network packet, Naive Bayes classifier, Data mining, Computer security, Machine learning, Support vector machine, World Wide Web, Pathology, Psychotherapist, Medicine, Psychology, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 5, 2023: 5, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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