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.

Cloud attack provenance is a well-established industrial practice for assuring transparency and accountability for a service provider to tenants. However, the multi-tenancy and self-service nature coupled with the sheer size of a cloud implies many unique challenges to cloud forensics. Although Virtual Machine Introspection (VMI) is a powerful tool for attack provenance due to the privilege isolation, the stealthiness of state-of-the-art attacks and the lack of precise information make existing attack provenance solutions difficult to fulfill real-time forensics when tracking enormous suspicious behaviors. To this end, we propose an instruction-level tracing framework for inspecting the presence of attacks by dynamically tracking shared processor hardware event patterns and analyzing the attack traces. To overcome the challenges of real-time detection and provenance, we advocate Last Branch Record (LBR) profiling, to extract the suspicious execution flows. With the hardware assistance and software-based virtualization introspection, we show that the framework can provide an effective response to threats in different cases, thereby enabling a quick attack provenance with high fidelity. The evaluation shows that our prototype introduces negligible performance penalties.

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low‐level adaptation methods that combine one detector with an additional dehazing network and (ii) high‐level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question—if combining both low‐level adaptation and high‐level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High‐Low Adaptation Object Detection paradigm (HLA‐HOD) in hazy weather conditions. By combining both low‐level adaptation and high‐level adaptation, HLA‐HOD achieves superior performance on hazy images without requiring ground‐truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state‐of‐the‐art low‐level and high‐level adaptation methods by a large margin both quantitatively and qualitatively.

A lightweight YOLOv8 variant, CIMB-YOLOv8, is proposed to address challenges in remote sensing object detection, such as complex backgrounds and multi-scale targets. The method enhances detection accuracy while reducing computational costs through two key innovations: Contextual Multi-branch Fusion: Integrates a space-to-depth multi-branch pyramid (SMP) to capture rich contextual features, improving small target detection by 1.2% on DIOR; Lightweight Architecture: Employs Lightweight GroupNorm Detail-enhance Detection (LGDD) with shared convolution, reducing parameters by 14% compared to YOLOv8n. Extensive experiments on DIOR, DOTA, and NWPU VHR-10 datasets demonstrate the model’s superiority, achieving 68.1% mAP on DOTA (+0.7% over YOLOv8n) and 82.9% mAP on NWPU VHR-10 (+1.7%). The model runs at 118.7 FPS on NVIDIA 3090, making it well-suited for real-time applications on resource-constrained devices. Results highlight its practical value for remote sensing scenarios requiring high-precision and lightweight detection.

A cryptosystem with non-commutative platform groups based on conjugator search problem was recently introduced at Neural Computing and Applications 2016. Its versatility was illustrated by building a public-key encryption scheme. We propose an algebraic key-recovery attack in the polynomial computational complexity. Furthermore, we peel off the encryption and decryption process and propose attack methods for solving the conjugator search problem over the given non-abelian group. Finally, we provide corresponding practical attack examples to illustrate the attack methods in our cryptanalysis, and provide some improved suggestions.