COMPATIVE ANALYSIS OF DEEP LEARNING MODELS FOR DDOS ATTACKS DETECTION

Abstract

Recently, Distributed Denial of Service(DDOS) attacks have been on the rise and come in very many forms costing many technology firms a lot of time and money. In this study, deep learning models were compared in terms of performance, to solve the problem of detecting these attacks. The first step to mitigating DDOS attacks is by first identifying them, which serves as a toll order. This report used two deep learning models: the Deep Feed Forward (DFF) algorithm and a hybrid containing a CNN with BiLSTM (bidirectional long short-term memory). To compare these algorithms, the “DDoS Botnet Attack on IoT a71a0b42-4” dataset available on Kaggle was chosen. The dataset was undergone various evaluations to find out the performance metrics between the two algorithms. From the simulations conducted, DFF was found to have an accuracy of 87.2% with detecting the time of 0.8 seconds, while the CNN-Bi-LSTM was found to have an accuracy of 94.6% with detecting the time of 1.4 seconds.