Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence
Rahul Rajendra Papalkar, Abrar S. Alvi · Network Computation in Neural Systems · 2024
In the Internet of Things (IoT), the security of information between network transmissions is very important since the system stores data in data storage and is performed by the exchange of network information about things. DDoS in an IoT network is an attack that targets the availability of the servers by flooding the communication channel with impersonated requests coming from distributed IoT devices. To overcome the above-mentioned issue, this research proposed a Creative Swagger (CS) Optimized Deep Convolutional Neural Network (DeepCNN) that detects and mitigates DDoS attacks. The CS algorithm is designed by fusing the distinctive behaviour of the Swagger with the innovative concepts of the civilized creature, which is used to effectively tune the parameters of Deep CNN to improve the detection accuracy of DDoS attacks. For initial verification, a blacklist table is used and the verification includes checking IP address and other pertinent attributes. The proposed CS-optimized Deep CNN model obtains high effectiveness by attaining an accuracy of 97.07%, sensitivity of 97.23%, and specificity of 96.91% at 80% of the training data for utilizing UNSW-NB15 Dataset. Moreover, this method provides the best solution for detecting DDoS attacks in IoT platforms with higher robustness.