Network Attack Mitigation in IoT-SDN Using Deep Recurrent Residual Neural Network

The large-scale networking is changing with more flexibility and manageability given by the Internet of Things (IoT) and Software-Defined Networking (SDN). However, they also present unique security challenges, especially when hackers occasionally exploit SDN infrastructure through IoT devices. IoT-SDN systems raise many security questions given their complexity and interconnectedness. This work proposes a deep learning-based novel, more notably Deep Recurrent Residual Learning (DRRL), sto improve network security in IoT-SDN systems. Resilient learning allows DRRL to effectively detect anomalies and targets by recording and altering temporal information. Using real-time analytics, this method continuously examines network traffic, detects odd activity, and responds fast to threats. Residual DRRL reduces false positives relative to standard approaches and enhances attack detection accuracy in simulated IoT-SDN systems, therefore reaching a 98.5% threat detection accuracy with a 2.3% false positive rate. The results show rapid implementation of security mechanisms, thereby reducing threat exposure.