Modified Flower Pollination Algorithm for ICMPv6-Based DDoS Attacks Anomaly Detection

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4*1038 unique IP addresses of devices in the network. IPv6 needed a number of protocols such as the Address Auto-configuration scheme and Internet Control Message Protocol (ICMPv6), IPv6 is vulnerable to numerous attacks such as Denial of Service (DoS) and Distributed Denial of Service (DDoS), DDoS is one of the most dangerous attacks carried out through ICMPv6 messages that impose security and pecuniary implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting ICMPv6 DDoS attacks using a modified Flower Pollination Algorithm (MFPA). The main purpose of the proposed method, first MFPA is to select the most relevant features from the ICMPv6 dataset to the detect ICMPv6 DDoS attacks. Second, an enhanced detecting DDoS flooding attacks model based on a multi-objective FPA has proposed. The results show that the first proposed method (MFPA) achieved the best accuracy equal to 97.96% with 10 features and the second proposed method Multi-Objective FPA achieved the best accuracy 97.01% with 5 features.