Labeled flow-based dataset of ICMPv6-based DDoS attacks
作者: Omar E. ElejlaMohammed AnbarBahari BelatonShady Hamouda
作者单位: 1School of Computer Science, Universiti Sains Malaysia
2National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia
3Emirates College of Technology
刊名: Neural Computing and Applications, 2019, Vol.31 (8), pp.3629-3646
来源数据库: Springer Nature Journal
DOI: 10.1007/s00521-017-3319-7
关键词: Intrusion detectionDataset generationICMPv6-based DDoS attacksFlow-based datasetsFeatures extraction
英文摘要: Abstract(#br)DDoS attacks that depend on Internet Control Message Protocol version 6 (ICMPv6) are one of the most commonly performed IPv6 attacks against today’s IPv6 networks. A few detection systems were proposed to detect these attacks based on self-generated datasets. These datasets used an unsuitable representation that depends on packets format as well as they include non-qualified features which lead to false alerts if the systems are applied in real networks. Moreover, most of the existing datasets are unavailable for other researchers’ usage due to their author’s privacy issues. The objective of this paper is benchmarking datasets of ICMPv6-based DDoS attacks to be used for the tuning, evaluations, and comparisons of any detection system of the attacks. The datasets setup is...
全文获取路径: Springer Nature  (合作)
影响因子:1.168 (2012)