Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

Internet of Things (IoTs) provides better solutions in various fields, namely healthcare, smart transportation, home, etc. Recognizing Denial of Service (DoS) outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems. Deep learning (DL) models outperform in detecting complex, non-linear relationships, allowing them to effectually severe slight deviations from normal IoT activities that may designate a DoS outbreak. The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection, permitting proactive reduction events to be executed, hence securing the IoT network's safety and functionality. Subsequently, this study presents pigeon-inspired optimization with a DL-based attack detection and classification (PIODL-ADC) approach in an IoT environment. The PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service (DDoS) attack detection in an IoT platform. Initially, the PIODLADC model utilizes Z-score normalization to scale input data into a uniform format. For handling the convolutional and adaptive behaviors of IoT, the PIODL-ADC model employs the pigeon-inspired optimization (PIO) method for feature selection to detect the related features, considerably enhancing the recognition's accuracy. Also, the Elman Recurrent Neural Network (ERNN) model is utilized to recognize and classify DDoS attacks. Moreover, reptile search algorithm (RSA) based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method. A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC method. The experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models, with a maximum accuracy of 99.81%.