Deep Shallow network with LSTM for detecting attacks in IoT networks and preserving privacy based on Adaptive hybrid encryption algorithm

Abstract

In this article, a new attack detection and privacy preservation framework is implemented to identify the attacks present in IoT networks and preserve the information from various attacks during transmission. Initially, the optimal features are selected from the garnered data by using the Adaptive Random Index-based Sea Lion Optimization Algorithm (ARI-SLOA). Consequently, the resultant features are given to a Deep Shallow Network with Long Short-Term Memory (DSN-LSTM) for attack identification. The attacks present in the IoT are mitigated during data transmission, and thus the data is highly secured. The designed encryption scheme is implemented for preserving the privacy of the information, where Adaptive Hybrid Attribute-based Encryption with an Advanced Encryption System (AHABE-AES) is utilized for privacy preservation. Here, the parameters of the AHABE-AES are optimized by the ARI-SLOA. The developed framework’s performance is examined with other existing approaches. From the results, the suggested framework obtained 95% accuracy, 8% FDR and 4% FNR rates. The outcomes obtained from the developed system ensure that this designed strategy is more robust and effective than other related models.