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lssue 7, 2025
Editors-in-Chief
Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 2095-9184
CN 33-1389/TP
IF (2024 JCR) 2.9
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Volume 26 Issue 8，2025
Regular Papers
Abstract：Noncooperative computer systems and network confrontation present a core challenge in cyberspace security. Traditional cybersecurity technologies predominantly rely on passive response mechanisms, which exhibit significant limitations when addressing real-world complex and unknown threats. This paper introduces the concept of "active cybersecurity," aiming to enhance network security not only through technical measures but also by leveraging strategy-level defenses. The core assumption of this concept is that attackers and defenders, in the context of network confrontations, act as rational decision-makers seeking to maximize their respective objectives. Building on this observation, this paper integrates game theory to analyze the interdependent relationships between attackers and defenders, thereby optimizing their strategies. Guided by this foundational idea, we propose an active cybersecurity model involving intelligent threat sensing, in-depth behavior analysis, comprehensive path profiling, and dynamic countermeasures, termed SAPC, designed to foster an integrated defense capability encompassing threat perception, analysis, tracing, and response. At its core, SAPC incorporates theoretical analyses of adversarial behavior and the optimization of corresponding strategies informed by game theory. By profiling adversaries and modeling confrontation as a "game," the model establishes a comprehensive framework that provides both theoretical insights into and practical guidance for cybersecurity. The proposed active cybersecurity model marks a transformative shift from passive defense to proactive perception and confrontation. It facilitates the evolution of cybersecurity technologies toward a new paradigm characterized by active prediction, prevention, and strategic guidance.Keywords：Active cybersecurity;Intelligent threat sensing;In-depth behavior analysis;Comprehensive path profiling;Dynamic countermeasures2|0|0Updated：2025-09-04Regular Papers
Abstract：Mimic active defense technology effectively disrupts attack routes and reduces the probability of successful attacks by using a dynamic heterogeneous redundancy (DHR) architecture. However, current approaches often overlook the adaptability of the adjudication mechanism in complex and variable network environments, focusing primarily on system security while neglecting performance considerations. To address these limitations, we propose an output difference feedback and system benefit control based DHR architecture. This architecture introduces an adjudication mechanism based on output difference feedback, which enhances adaptability by considering the impact of each executor's output deviation on the global decision. Additionally, the architecture incorporates a scheduling strategy based on system benefit, which models the quality of service and switching overhead as a bi-objective optimization problem, balancing security with reduced computational costs and system overhead. Simulation results demonstrate that our architecture improves adaptability towards different network environments and effectively reduces both the attack success rate and average failure rate.Keywords：Mimic defense;Adjudication mechanism;Scheduling strategy;Executor output difference;System benefit2|1|0Updated：2025-09-04Regular Papers
Abstract：Temporal attention mechanisms are essential for video action recognition, enabling models to focus on semantically informative moments. However, these models frequently exhibit temporal infidelity—misaligned attention weights caused by limited training diversity and the absence of fine-grained temporal supervision. While video-level labels provide coarse-grained action guidance, the lack of detailed constraints allows attention noise to persist, especially in complex scenarios with distracting spatial elements. To address this issue, we propose temporal fidelity enhancement (TFE), a competitive learning paradigm based on the disentangled information bottleneck (DisenIB) theory. TFE mitigates temporal infidelity by decoupling action-relevant semantics from spurious correlations through adversarial feature disentanglement. Using pre-trained representations for initialization, TFE establishes an adversarial process in which segments with elevated temporal attention compete against contexts with diminished action relevance. This mechanism ensures temporal consistency and enhances the fidelity of attention patterns without requiring explicit fine-grained supervision. Extensive studies on UCF101, HMDB-51, and Charades benchmarks validate the effectiveness of our method, with significant improvements in action recognition accuracy.Keywords：Action recognition;Disentangled information bottleneck;Temporal modeling;Temporal fidelity2|1|0Updated：2025-09-04Regular Papers
Abstract：Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human brain tissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidly estimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, these methods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limiting the application scenarios of such methods. To address this issue, we develop a new deep neural network called q-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTs under flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistency strategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, a q-space-coordinate fusion (QCF) module is introduced which efficiently embeds q-space-coordinates into multiscale features of the corresponding DW images by linearly adjusting the feature maps along the channel dimension, thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense (MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilities by using dual-branch convolutions with different kernel sizes to extract features at different scales. Compared to state-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusion tensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Compared to state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% on fractional anisotropy and around 25% on mean diffusivity.Keywords：Diffusion tensor imaging;Diffusion tractography;Deep learning;Fast diffusion tensor estimation;Q-space-coordinate information2|0|0Updated：2025-09-04Regular Papers
Abstract：End-to-end object detection methods have attracted extensive interest recently since they alleviate the need for complicated human-designed components and simplify the detection pipeline. However, these methods suffer from slower training convergence and inferior detection performance compared to conventional detectors, as their feature fusion and selection processes are constrained by insufficient positive supervision. To address this issue, we introduce a novel query-selection encoder (QSE) designed for end-to-end object detectors to improve the training convergence speed and detection accuracy. QSE is composed of multiple encoder layers stacked on top of the backbone. A lightweight head network is added after each encoder layer to continuously optimize features in a cascading manner, providing more positive supervision for efficient training. Additionally, a hierarchical feature-aware attention (HFA) mechanism is incorporated in each encoder layer, including in- and cross-level feature attention, to enhance the interaction between features from different levels. HFA can effectively suppress similar feature representations and highlight discriminative ones, thereby accelerating the feature selection process. Our method is highly versatile in accommodating both CNN- and Transformer-based detectors. Extensive experiments were conducted on the popular benchmark datasets MS COCO, CrowdHuman, and PASCAL VOC to demonstrate the effectiveness of our method. The results showed that CNN- and Transformer-based detectors using QSE can achieve better end-to-end performance within fewer training epochs.Keywords：End-to-end object detection;Query-selection encoder;Hierarchical feature-aware attention2|1|0Updated：2025-09-04Regular Papers
Abstract：Large language models (LLMs) exhibit remarkable capabilities in various natural language processing tasks, such as machine translation. However, the large number of LLM parameters incurs significant costs during inference. Previous studies have attempted to train translation-tailored LLMs with moderately sized models by fine-tuning them on the translation data. Nevertheless, when performing translations in zero-shot directions that are absent from the fine-tuning data, the problem of ignoring instructions and thus producing translations in the wrong language (i.e., the off-target translation issue) remains unresolved. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability of translation-tailored LLMs, particularly for maintaining accurate translation directions. We first fine-tune LLMs on the translation data to elicit basic translation capabilities. At the second stage, we construct instruction-conflicting samples by randomly replacing the instructions with the incorrect ones. Then, we introduce an extra unlikelihood loss to reduce the probability assigned to those samples. Experiments on two benchmarks using the LLaMA 2 and LLaMA 3 models, spanning 16 zero-shot directions, demonstrate that, compared to the competitive baseline—translation-finetuned LLaMA, our method could effectively reduce the off-target translation ratio (up to -62.4 percentage points), thus improving translation quality (up to +9.7 bilingual evaluation understudy). Analysis shows that our method can preserve the model's performance on other tasks, such as supervised translation and general tasks. Code is released at https://github.com/alphadl/LanguageAware_Tuning.Keywords：Zero-shot machine translation;Off-target issue;Large Language Model;Language-aware instruction tuning;Instruction-conflicting sample2|1|0Updated：2025-09-04
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