IPv6 was designed to solve the issue of adopting IPv4 addresses by presenting a large number of address spaces. Currently, many networking devices consider IPv6 as a supportive IPv6-enabled device that includes routers, notebooks, personal computers, and mobile phones. Security has increasingly become a significant issue in exploiting networks and obtaining the benefits of IPv6. One of the important protocols in IPv6 implementation that is used for neighbor and router discovery is ICMPv6. However, this protocol can be used by attackers to deny network services through ICMPv6 DDoS flooding attacks that decrease the network performance. To solve this problem, this study proposes an intelligent ICMPv6 DDoS flooding-attack detection framework using back-propagation neural network (v6IIDS) in IPv6 networks. This study also explores and analyzes the detection accuracy of the proposed v6IIDS framework. The effectiveness of the v6IIDS framework is demonstrated by using real data-sets obtained from an NAv6 laboratory. The data-set traffic is based on a test-bed environment created on the basis of certain parameters used as inputs to generate a new data-set. The results prove that the proposed framework is capable of detecting ICMPv6 DDoS flood attacks with a detection accuracy rate of 98.3%.

The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.

ICMPv6 Flood Attack Detection using DENFIS Algorithms

A Survey of Botnet-Based DDoS Flooding Attacks of Application Layer: Detection and Mitigation Approaches: 10.4018/978-1-5225-0105-3.ch003: A Botnet can be used to launch a cyber-attack, such as a Distributed Denial of Service (DDoS) attack, against a target or to conduct a cyber-espionage