Improved Genetically Optimized Neural Network Algorithm for Classification of Distributed Denial of Service Attack
Keywords:DDoS, genetic algorithm, neural network, naïve bayes, machine learning
This paper proposes a classification of distributed denial of service (DDOS) attack using neural network-based genetic algorithm (NNGA). The genetic algorithm was used to optimize neural network for the detection of DDoS attacks in order to improve the effectiveness and efficiency of classification accuracy and performance. In order to improve the NNGA, a fitness function was introduced in genetic algorithm that improved the performance of NNGA. The features of DDOS attacks from KDD 99 intrusion detection datasets were obtained to train the NNGA. The results show the improved genetically optimized neural network algorithm has better accuracy and lower false positive rate in comparison with the conventional neural network.