Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity‐based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. This algorithm uses the correlation between features to select the best subset. Then, a hybrid partitioning strategy is introduced in the negative selection algorithm (NSA), which divides the feature space into a grid based on the sample distribution density and generates specific candidate detectors in the boundary grid to effectively mitigate the holes caused by boundary diversity. Finally, the NSA is improved by self‐set clustering and a novel gray wolf optimizer to achieve adaptive adjustment of the detector radius and position. The results show that the proposed NSA algorithm based on mixed hierarchical division and gray wolf optimization (MDGWO‐NSA) achieves a higher detection rate, lower false alarm rate, and better generation quality than other network anomaly detection algorithms.

Previous studies have specifically demonstrated that incorporating high-quality side-information can notably enhance the steganographic security of JPEG images. To obtain more precise side-information estimates, researchers utilize sophisticated deblocking or denoising filters, which enhance security but require a substantial amount of time. The challenge lies in achieving a significant improvement in security and efficiency. This paper introduces a steganographic network named WTSNet, which leverages wavelet transform for estimating side-information through iterative optimization in both the pixel and wavelet domains. The wavelet domain is purposely designed to capture intricate high-frequency texture details and facilitate precise estimation of the side-information. Furthermore, the embedding cost is adjusted based on the polarity of the estimated rounding error, ensuring both secure and efficient performance. Experimental results demonstrate that the proposed scheme substantially enhances the security of conventional JPEG steganography, which relies on additive distortion. Moreover, it outperforms the state-of-the-art JPEG steganography methods based on estimating side-information while consuming less time.

Generative steganography has gained significant attention due to its cover-independent nature, which allows it to evade a broad range of steganalysis techniques. However, existing methods often suffer from poor extraction accuracy at high embedding rates and limited robustness to real-world noise. To address these limitations, we propose the Controllable Generative Image Steganography (CGIS) framework, introducing two bidirectional mapping mechanisms: the Resampling-based RS mapping and the Normal Distribution-based ND mapping. These significantly enhance extraction accuracy even at high embedding rates. Unlike prior stochastic or training-dependent approaches, CGIS utilizes a training-free and deterministic diffusion process, fundamentally distinguishing it from existing methods and ensuring superior robustness. Additionally, a Gaussian Mixture Model-based image denoiser (GMM-ID) ensures the model’s resilience to network noise and compression-induced distortion. Experimental results demonstrate that CGIS outperforms existing methods in terms of extraction accuracy (97.75%), detection resistance (96.23%), and image distortion (FID: 2.36), demonstrating superior performance for practical deployment.