Rapid advancements of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) pose serious security issues by revealing secret data. Therefore, security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time. Practically, AI techniques can be utilized to design image steganographic techniques in IIoT. In addition, encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access. In order to accomplish secure data transmission in IIoT environment, this study presents novel encryption with image steganography based data hiding technique (EIS-DHT) for IIoT environment. The proposed EIS-DHT technique involves a new quantum black widow optimization (QBWO) to competently choose the pixel values for hiding secrete data in the cover image. In addition, the multi-level discrete wavelet transform (DWT) based transformation process takes place. Besides, the secret image is divided into three R, G, and B bands which are then individually encrypted using Blowfish, Twofish, and Lorenz Hyperchaotic System. At last, the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover image. In order to validate the enhanced data hiding performance of the EIS-DHT technique, a set of simulation analyses take place and the results are inspected interms of different measures. The experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.
The recent adoption of satellite technologies, unmanned aerial vehicles (UAVs) and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher quality. But, security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security volumes. This study presents an Archimedes Optimization with Deep Learning based Aerial Image Classification and Intrusion Detection (AODL-AICID) technique in secure UAV networks. The presented AODL-AICID technique concentrates on two major processes: image classification and intrusion detection. For aerial image classification, the AODL-AICID technique encompasses MobileNetv2 feature extraction, Archimedes Optimization Algorithm (AOA) based hyperparameter optimizer, and backpropagation neural network (BPNN) based classifier. In addition, the AODL-AICID technique employs a stacked bi-directional long short-term memory (SBLSTM) model to accomplish intrusion detection for cybersecurity in UAV networks. At the final stage, the Nadam optimizer is utilized for parameter tuning of the SBLSTM approach. The experimental validation of the AODL-AICID technique is tested and the obtained values reported the improved performance of the AODL-AICID technique over other models.
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