Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

Abstract

IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.