The upcoming 5G heterogeneous networks (HetNets) have attracted much attention worldwide. Large amounts of high velocity data can be transported by using the bandwidth spectrum of HetNets, yielding both great benefits and several concerning issues. In particular, great harm to our community could occur if the main visual information channels, such as images and videos, are maliciously attacked and uploaded to the internet, where they can be spread quickly. Therefore, we propose a novel framework as a digital forensics tool to protect end users. It is built based on deep learning and can realize the detection of attacks via classification. Compared with the conventional methods and justified by our experiments, the data collection efficiency, robustness, and detection performance of the proposed model are all refined. In addition, assisted by 5G HetNets, our proposed framework makes it possible to provide high-quality real-time forensics services on edge consumer devices (ECE) such as cell phones and laptops, which brings colossal practical value. Some discussions are also carried out to outline potential future threats.

Due to the security weaknesses of chaos-based pseudorandom number generators, in this paper, a new pseudorandom number generator (PRNG) based on mixing three-dimensional variables of a cat chaotic map is proposed. A uniformly distributed chaotic sequence by a logistic map is used in the mixing step. Both statistical tests and a security analysis indicate that our PRNG has good randomness and is more complex than any one-dimensional variable of a cat map. Furthermore, a new image encryption algorithm based on the chaotic PRNG is provided to protect the content of artwork images. The core of the algorithm is to use the sequence generated by the pseudorandom number generator to achieve the process of disruption and diffusion of the image pixels, so as to achieve the effect of obfuscation and encryption of the image content. Several security tests demonstrate that this image encryption algorithm has a high security level.

Chaotic systems are widely used in many scientific fields for their dynamic characteristics. This study proposes a new delayed coupling method, which not only disturbs the control coefficient in chaotic maps but also affects their function structure, such that using this improved method will produce chaotic maps with better effect. The numerical simulation results prove that the delayed coupling method can greatly improve the chaotic characteristics of chaotic maps. Furthermore, an image encryption algorithm based on the delayed coupling Logistic map is proposed. Several numerical simulations indicate that the image encryption algorithm has a high level of security, and can compete with other encryption algorithms.

While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.