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Security and Safety 2023年第3期2卷 83-106页

作者：徐钫洲张网羌卫中金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution ***,malware can employ evasive techniques to detect the analysis environment and alter its behavior *** known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown *** this paper,we present Antitoxin,a prototype for automatically exploring evasive *** utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive *** probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain ***,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path *** is achieved through forced execution,which forcefully sets the outcomes of branches on selected ***,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration ***,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual *** experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic *** probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and signific

关键词： Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection

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A Hybrid Routing Algorithm based on Destruction Resistance Cost and Reliability in Flying Ad-Hoc Networks 7

A Hybrid Routing Algorithm based on Destruction Resistance C...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Guo, Kun Wang, Dongbin Zhi, Hui Gao, Yibo Zhang, Tao Lv, Tiejun Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Beijing University of Posts and Telecommunications National Engineering Laboratory For Mobile Network Beijing China TravelSky Technology Limited Beijing China Beijing University of Posts and Telecommunications Cyberspace Security Research Center Peng Cheng Laboratory Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665409506

Existing routing algorithms in Flying Ad-Hoc Net- works (FANETs) lack the optimization consideration of destruction resistance cost caused by routing recovery when some nodes fail suddenly. In this paper, we propose a hybrid routing algorithm based on destruction resistance cost and reliability. Based on the real-time network topology and network state, the algorithm can provide a multi-parameters-based routing path with low destruction resistance cost for communication transmission. It can ensure that the main routing path can be quickly recovered after interruption and reduce the process of discovering new routing paths. We have constructed a variety of simulation scenarios to analyze the proposed algorithm and the existing algorithms. The simulation results show that the pro- posed algorithm has good performance in reducing destruction resistance cost. © 2021 IEEE.

关键词： Routing algorithms

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A Double-Shell Structured Ransomware Defense Method Tailored for the RaaS Model 3rd

A Double-Shell Structured Ransomware Defense Method Tailored...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Chen, Monan Ji, Tiantian Li, Shudong Zhang, Yuntao Wang, Tian Mao, Kai Sun, Yanwei Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Peng Cheng Laboratory Shenzhen China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9789819645022

Ransomware attacks have become a significant cybersecurity threat, with the Ransomware as a service (RaaS) paradigm enabling attackers with limited technical expertise to launch sophisticated campaigns. There is currently a lack of systematic research specifically targeting RaaS-type ransomware. This paper analyzes the operational mechanisms of RaaS ransomware, including its industry chain, attack processes, and technical characteristics. We propose a formalized RaaS attack model, providing a clear understanding of its operational methods. To address the limitations of existing defense methods, we introduce an innovative double-shell structured defense model. Utilizing Hook technology and a token verification mechanism, it effectively prevents ransomware from encrypting user data. We demonstrate the effectiveness of this model through experimental validation with a document protection program named RansomShield, achieving a 100% success rate in defending against real ransomware attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer viruses

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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Rate Control for VVC Intra Coding with Simplified Cubic Rate-Distortion Model

Rate Control for VVC Intra Coding with Simplified Cubic Rate...

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IEEE Workshop on Multimedia Signal Processing

作者： Yizhao Wang Jiaqi Zhang Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

In this paper, we propose a simplified cubic polynomial R-D model with corresponding rate control methods for Versatile Video Coding (VVC) intra frame coding. First, we explore the rate-distortion (R-D) characteristics of VVC intra coding. By comparing several potential R-D modeling approaches, a new intra coding R-D model has been proposed based on the simplified cubic polynomial function. Subsequently, we derive the corresponding $R-\lambda$ model and introduce a complexity measurement to improve the performance of intra frame rate control. Furthermore, we propose a Coding Tree Unit (CTU)-level rate control method based on the newly proposed R-D model and further develop a pre-compression-based approach on this basis. Experimental results show that the proposed method can achieve 1.87% and 0.55% bit rate reduction for All-Intra (AI) and Random-Access (RA) configurations over the original rate control in VVC Test Model (VTM), while the computational complexity increment is negligible. Meanwhile, the enhanced bit rate accuracy from rate control has been observed in the proposed methods.

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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From Passive to Active: Near-optimal DNS-based Data Exfiltration Defense Method Based on Sticky Mechanism

From Passive to Active: Near-optimal DNS-based Data Exfiltra...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jiawen Diao Binxing Fang Xiang Cui Zhongru Wang Tian Wang Shouyou Song Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications) Ministry of Education Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Cyberspace Studies Beijing China Beijing DigApis Technology Co. Ltd Beijing China

DNS-based data exfiltration has become increasingly popular among advanced persistent threat (APT) attackers owing to the ubiquity and penetrability of the DNS protocol. AI-powered methods solve the defect that attackers can easily bypass because of the fixed threshold and weight in rule matching while still suffer from several issues. Such as the lack of malware samples for training, the amplified impact of even low FPR present enormous obstacles to applying the model in real-world *** present a method to generate malicious traffic covering an extensive sample space based on Tactics, Techniques, and Procedures (TTPs). We then propose a sticky mechanism, which transforms certain decision-making into dynamic human-computer interaction decision-making, to verify the suspicious hosts recognized by the AI model. The experimental results demonstrate the superiority of our model by identifying eight kinds of real attacks precisely. The good performance on real-world traffic shows our method is a solid foothold for applying AI-powered detection to practical applications.

关键词： Training Human computer interaction Data privacy Protocols Decision making Transforms Solids

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RIPPB: A Robust and Improved PBFT Protocol for Blockchain

RIPPB: A Robust and Improved PBFT Protocol for Blockchain

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meng Ding Heng He Rui Qiao Xiaohu Zhou School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Computing Engineering and Built Environment Birmingham City University Birmingham UK

ISBN: (纸本)9781665409858

In this paper, we propose RIPPB, a robust and improved PBFT protocol for Blockchain, to improve the quality of service for blockchain applications. At RIPPB, we introduce reputation for nodes and use reputation as the voting weight for node consensus to go against Sybil attacks. And we design a novel improved logistic regression model to assign reputations to nodes based on their behavior involved in block generation. With this model, nodes performing Stake-grinding attacks and Bribery attacks, face high reputation penalties, which is unacceptable for malicious nodes to reduce the probability of launching these attacks. In addition, RIPPB adds a pre-commit phase to the PBFT algorithm to reduce the number of inter-node communications, thus improving the performance of the protocol. Cost-of-attack analysis shows the high robustness of our protocol. Experimental results show a 28% improvement in the throughput of our protocol over PBFT.

关键词： Industrial electronics Protocols Quality of service Throughput Robustness Blockchains Behavioral sciences

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Two-stream Hierarchical Similarity Reasoning for Image-text Matching

arXiv

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arXiv 2022年

作者： Chen, Ran Wang, Hanli Wang, Lei Kwong, Sam The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China DeepBlue Academy of Sciences Shanghai200336 China The Department of Computer Science City University of Hong Kong Hong Kong

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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