Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks

Applied Sciences

Shady Hamouda¹  Serri Faisal¹  Omar E. Elejla²  Abdullah Ahmed Bahashwan³  Mohammed Anbar³  Iznan H. Hasbullah³ 

[1] Department of Business Information Technology, Liwa College of Technology, Abu Dhabi 51133, United Arab Emirates;Department of Computer Science, Al-Aqsa University, Gaza 4051, Palestine;National Advanced IPv6 (NAv6), Universiti Sains Malaysia, Gelugor, Penang 11800, Malaysia;

关键词: ICMPv6 flooding DDoS attacks;    deep learning;    machine learning;    intrusion detection system;   

DOI  :  10.3390/app12126150

来源: DOAJ

【 摘 要 】

Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches.

【 授权许可】

Unknown