Deep Learning Feature Fusion Approach for an Intrusion Detection System in SDN-Based IoT Networks
Vinayakumar Ravi, Rajasekhar Chaganti, Mamoun Alazab
Abstract
A survey of the literature shows that the number of IoT attacks are gradually growing over the years due to the growing trend of Internet-enabled devices. Software defined networking (SDN) is a promising advanced computer network technology that supports IoT. A network intrusion detection system is an essential component in the SDN-IoT network environment to detect attacks and classify the attacks into their categories. Following, this work proposes a deep-learning-based approach that detects attacks and classifies them into their attack categories. The model extracts the internal feature representations from the gated recurrent unit (GRU) deep learning layers; further, the optimal features were extracted using kernel principal component analysis (kernel-PCA). Next, features were fused together, and attack detection and its classification is done using the fully connected network. The proposed feature fused GRU network has achieved better performance than the GRU model and other well-known classical machine-learning-based models. The proposed method can be used in real time to effectively monitor the network traffic in the SDN-IoT environment to proactively alert about possible attacks and classify them into their attack categories.