The security of digital images has become increasingly important as information technology has advanced considerably. To ensure image security and improve the efficiency of image encryption, a novel color image encryption algorithm is proposed in this paper. Firstly, a novel three-dimensional chaotic system with extreme multistability is proposed and employed to generate the chaotic sequences to be used in the permutation and diffusion processes. Secondly, the proposed bidirectional spiral transformation is used to permute the R, G and B components extracted from the color plain image. Thirdly, the permuted pixel sequence is dynamically encoded into a DNA sequence. To further increase the permutation effect, the DNA sequence is permuted again using the index sequence. Finally, the dynamical DNA operations are performed to diffuse the permuted DNA sequence, and the designed look-up table method is used to quickly obtain the DNA operation results. The key space of the proposed algorithm is 10²⁶⁶, which is large enough to resist the brute-force attacks. The average entropy value of the cipher image of Peppers is 7.9971, which is much closer to the theoretical entropy value 8. The dynamic DNA operations process only takes up 0.032349s, which shows the advantage of the proposed look-up table method. Therefore, the proposed algorithm can be applied to the application that require high confidentiality and real-time.

Due to the competitive relationship among different smart factories, equipment manufacturers cannot integrate the private information of all smart factories to train the intelligent manufacturing equipment fault prediction model and improve the accuracy of intelligent manufacturing equipment fault detection. The use of a low fault recognition rate model for smart factories will cause additional losses for them. In this work, we propose a blockchain-based privacy information security sharing scheme in Industrial Internet of Things (IIoT) to solve the sharing problem of private information in smart factories. Firstly, we abstract smart factories as edge nodes and build decentralized, distributed trusted blockchain networks based on Ethereum clients on simulated edge devices and propose an Intelligent Elliptic Curve Digital Signature Algorithm (IECDSA) to guarantee the ownership of shared information by edge nodes. Secondly, we propose the Reputation-based Delegated Proof of Stake (RDPoS) consensus algorithm to improve the security and reliability of the Delegated Proof of Stake (DPoS) consensus algorithm. Furthermore, we design and implement an incentive mechanism based on information attributes to increase the motivation of edge nodes to share information. Finally, the proposed solution is simulated. Through theoretical and simulation experiments, it is proved that the blockchain-based privacy information security sharing scheme in IIoT can improve the enthusiasm of edge nodes to share information on the premise of ensuring the security of information sharing.

The quick advancement of multimedia technology has led to the widespread utilization of digital images across various industries. Meanwhile, image security has become an urgent issue in need of resolution. To guarantee image security as well fulfill the requirements of real-time image cryptosystems, a novel image encryption algorithm is proposed based on the designed improved iterative chaotic map with infinite collapses (ICMIC), Gray code and semi-pixel level permutation and diffusion. Firstly, two chaotic sequences are produced using the improved ICMIC and then they are used to perform scrambling and diffusion operations. Secondly, the plain image is encoded using Gray code and then we convert each Gray code into two semi-pixels. Thirdly, the designed semi-pixel level permutation is used to scramble the semi-pixel vector. Finally, the scrambled semi-pixel vector is diffused using the designed dynamic semi-pixel level diffusion. The performance analyses illustrate that the proposed algorithm possesses robust security and high efficiency, making it ideally suited for application in real-time image cryptosystems.

Accurate and real‐time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle‐to‐everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human‐computer interaction activities. At the same time, it requires high‐throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real‐time high‐quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial‐temporal graph convolution gated recurrent unit (GC‐GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC‐GRU model has better prediction performance than other baseline models and can obtain spatial‐temporal correlation in traffic lows better.