A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning

Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-physical system over the last decade. Defending against DDoS attack is not only challenging but also strategic. Tons of new strategies and approaches have been proposed to defend against different types of DDoS attacks. The ongoing battle between the attackers and defenders is full-fledged due to its newest strategies and techniques. Machine learning (ML) has promising outcomes in different research fields including cybersecurity. In this paper, ensemble unsupervised ML approach is used to implement an intrusion detection system which has the noteworthy accuracy to detect DDoS attacks. The goal of this research is to increase the DDoS attack detection accuracy while decreasing the false positive rate. The NSL-KDD dataset and twelve feature sets from existing research are used for experimentation to compare our ensemble results with those of our individual and other existing models

A Comparison of Neural Network Approaches for Network Intrusion Detection

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYNowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection