Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

Abstract

The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such cyberattacks. With this motivation, the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection (ECSADL-CAD) model for the Software-Defined Networking (SDN)-enabled IoT environment. The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT environment. To attain this, the ECSADL-CAD model initially pre-processes the data. In the presented ECSADL-CAD model, the Reinforced Deep Belief Network (RDBN) model is employed for attack detection. At last, the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes. A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model. The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.