<![CDATA[With the evolution of telecommunication core and access networks, the next generation networks leverages software defined networks (SDN) to provide flexi bility, scalability and centralized control. Denial of service (DoS)/distributed DoS (DDoS) attacks have been a major threat to next generation networks especially to the centralized architecture of SDNs. The ever-changing and dynamic nature of the DoS/DDoS attacks makes it challenging to detect and resolve them. The existing models to handle DoS/DDoS attacks often suffer from false positive rates and adaptability. In order to solve these problems, this study aims to create and apply sophisticated deep learning framework namely adversarial DBN-LSTM to accurately detect and classify various DoS/DDoS attack types. The proposed adversarial DBN-LSTM model is based on the generative adversarial networks. The proposed model uses generator to generate the adversarial attack and discrim inator to detect the attacks. The adversarial DBN-LSTM model is evaluated using a dataset specifically generated in a Mininet-based SDN controller environment to ensure relevance and practical applicability. The performance of the adver sarial DBN-LSTM is compared with other prevalent models. The adversarial DBN-LSTMmodelachieves accuracy about 99.4%. The proposed work achieves a breakthrough in identifying and preventing DoS/DDoS threats in relation to SDNenvironment.]]>
Copyrights © 2026
