Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers

Secure synchronization control for complex dynamic networks with event-triggered communication strategy under multi-channel denial-of-service attacks

This article addresses the secure synchronization problem for complex dynamical networks (CDNs) with observer-based event-triggered communication strategy (ETCS) under multi-channel denial-of-service attacks (MCDSAs). Due to external environmental interference, the observers are designed to accurately estimate the state of the network systems. Meanwhile, the impact of cyber attacks on system security is considered. Differing from other studies, MCDSAs in this paper are considered, which present the interference of malicious attacks on the communication topology, observers and controllers. Based on the characteristics of cyber attacks, the model for CDNs with nonperiodic switching topology is established, and the observer-based intermittent control is proposed to achieve synchronization of CDNs. To decrease the network communication frequency and reduce the risk of cyber attacks, a new ETCS depending on estimate states is designed. Then, using Lyapunov stability theory, several synchronization conditions depending on the status parameters of MCDSAs are deduced, which may estimate the allowable ranges for the duration and frequency of MCDSAs. From these theoretical results, it can be observed that the destructive power of network attacks increases exponentially with the increase of attack duration. Finally, from simulation experiments, the availability of the designed controller and communication strategy is verified, which can ensure the synchronization for CDNs with MCDSAs.

An FHN-HR Neuron Network Coupled With a Novel Locally Active Memristor and Its DSP Implementation

In this article, a novel locally active memristor (LAM) model is designed and its characteristics are studied in detail. Then, the LAM model is applied to couple FitzHugh-Nagumo (FHN) and Hindmarsh-Rose (HR) neuron. The simple neuron network is built to emulate connection of separate neurons and transmission of information from FHN neuron to HR neuron. The equilibrium point about this FHN-HR model is analyzed. Under the influence of varied parameters, dynamical characteristics for the model are explored with various analysis methods, including phase diagram, time series, bifurcation diagram, and Lyapunov exponent spectrum (LEs). The spectral entropy (SE) complexity and sequence randomness of the model are studied. In addition to observing chaotic and periodic attractors, multiple types of attractor coexistence and particular state transition phenomena are also found in the coupled FHN-HR model. Furthermore, geometric control is used for modulating the amplitude and offset of attractor and neuron firing signals, involving amplitude control and offset control. Finally, DSP implementation is finished, proving digital circuit feasibility of the FHN-HR model. The research imitates the coupling and information transmission between different neurons and has potential applications to secrecy or encryption.

LVGG-IE: A Novel Lightweight VGG-Based Image Encryption Scheme

Image security faces increasing challenges with the widespread application of computer science and artificial intelligence. Although chaotic systems are employed to encrypt images and prevent unauthorized access or tampering, the degradation that occurs during the binarization process in chaotic systems reduces security. The chaos- and DNA-based image encryption schemes increases its complexity, while the integration of deep learning with image encryption is still in its infancy and has several shortcomings. An image encryption scheme with high security and efficiency is required for the protection of the image. To address these problems, we propose a novel image encryption scheme based on the lightweight VGG (LVGG), referred to as LVGG-IE. In this work, we design an LVGG network with fewer layers while maintaining a high capacity for feature capture. This network is used to generate a key seed, which is then employed to transform the plaintext image into part of the initial value of a chaotic system, ensuring that the chaos-based key generator correlates with the plaintext image. A dynamic substitution box (S-box) is also designed and used to scramble the randomly shuffled plaintext image. Additionally, a single-connected (SC) layer is combined with a convolution layer from VGG to encrypt the image, where the SC layer is dynamically constructed by the secret key and the convolution kernel is set to 1×2. The encryption efficiency is simulated, and the security is analyzed. The results show that the correlation coefficient between adjacent pixels in the proposed scheme achieves 10-4. The NPCR exceeds 0.9958, and the UACI falls within the theoretical value with a significance level of 0.05. The encryption quality, the security of the dynamic S-box and the SC layer, and the efficiency are tested. The result shows that the proposed image encryption scheme demonstrates high security, efficiency, and robustness, making it effective for image security in various applications.

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.