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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Junpeng Qu Gaolei Du Yunge Sun Yong Fu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Quan Cheng Laboratory Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Aiming at the problem that malicious software uses encryption algorithm to hide its own malicious code and avoid detection, a dynamic detection technology based on Pin encryption algorithm is proposed. Use Pin to get the instruction flow of executable file running, then identify the function call in the instruction flow to get the function call framework, find the loop body in the calling function, and then carry out the encryption algorithm instruction detection and encryption algorithm constant detection on the loop body. If the detection passes, it can be considered that a component of the encryption algorithm is found. Then the complete encryption algorithm is found according to the generated function call framework. Experiments show that this method can effectively identify the encryption algorithms hidden in executable files.

关键词： Heuristic algorithms Software algorithms Cyberspace Data science Feature extraction Malware Encryption Pins

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An Encryption Algorithm Cycle Identification Method Based on Bit Execution

An Encryption Algorithm Cycle Identification Method Based on...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yunge Sun Gaolei Du Junpeng Qu Yong Fu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology(Shandong Academy of Sciences) Jinan China Quan Cheng Laboratory Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Ransomware uses symmetric encryption algorithms to lock user files to extort money. This paper proposes a cryptographic algorithm loop identification method based on bit-precise execution, aiming to solve the problem of identifying unknown loops. The structure of the loop body of encryption algorithms is fixed and usually includes nonlinear operations and linear operations. This article converts the loop body in the unknown software into a set of Boolean formulas, and converts the malware into a register data flow through the symbolic executor, thereby converting it into a set of Boolean formulas for execution. Execute known encryption algorithms with bit accuracy and establish a Boolean formula input-output relationship library for cryptographic components. The two sets of Boolean formulas are passed through a designed input matrix to achieve the Boolean formula mapping and input variable mapping of the confirmation algorithm, which can effectively judge the encryption algorithms in some malware.

关键词： Deformation Input variables Software algorithms Merging Data science Libraries Encryption Registers Ransomware Matrix converters

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An Interactive Attention Mechanism Fusion network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Gao, Yuan Li, Haokang College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang050000 China

ISBN: (纸本)9798350303780

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Sentiment analysis

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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An automated construction method for symmetric encryption round function models based on dynamic analysis

An automated construction method for symmetric encryption ro...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Gaolei Du Yunge Sun Junpeng Qu Yong Fu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Quan Cheng Laboratory Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. Data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption.

关键词： Analytical models Ciphers Tracking loops XML Data models Malware Encryption Registers Trajectory Pins

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Electric Energy Trading Mechanism of Distributed Electric Vehicles in Smart Grid 38

Electric Energy Trading Mechanism of Distributed Electric Ve...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Hu, Chunlin An, Dou Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xian710049 China Xi'an Jiaotong University Fac. of Electron. and Info. Eng. and the Moe Key Lab. for Intelligent Networks and Network Security Xian710049 China

ISBN: (纸本)9798350303636

With the continuous development of the smart grid and the increasing number of electric vehicles (EVs), the power transaction in the smart grid has become increasingly important in our daily life. This paper studies the distributed trading mechanism of electric vehicle energy in a smart grid. First, a non-cooperative game model is established among EV users. Each user can buy and sell power to both the central grid and other EV users. EV owners may act as buyers or sellers in the transaction according to their power availability. Next, this paper calculates the optimal utility function for EV users and obtains the optimal decision. The expression of the Nash equilibrium solution and the optimal user utility function are given. Then the efficiency of Nash equilibrium is analyzed. Finally, demand response management is carried out under the model. This paper designs two groups of comparative experiments. The first experiment compared daily electricity consumption and electricity charges of users before and after demand response management. The second experiment showed how the sum of electricity bills changed with the number of users who don't change their habits. © 2023 IEEE.

关键词： demand response management electricity transaction Nash equilibrium non-cooperative game

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$k$k-TEVS: A $ k$k-Times E-Voting Scheme on Blockchain With Supervision

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IEEE Transactions on Dependable and Secure Computing 2024年第3期22卷 2326-2337页

作者： Yang Liu Debiao He Min Luo Lianhai Wang Cong Peng School of Cyber Science and Engineering Wuhan University Wuhan China Institute of Information Technology Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

The e-vote is regarded as a way to express the opinion that the voters ask for. Actually, the e-vote could be applied wildly like questionnaire, survey and feedback. Moreover, the coexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storage costs for the field consisted of the vote group manager (GM) with its vote assistants (VA). Particularly the $k$ -times anonymous authentication ( $k$ -TAA) could also be a practical approach to preserve voters’ privacy and reduce the computation costs during the vote process. However, the e-vote scheme with pure $k$ -TAA strategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of $k$ -TAA with the update process for the e-vote on blockchain for supervision ( $ k$ -TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the $ k$ -TEVS framework. Finally, We implement $ k$ -TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme.

关键词： Blockchains Authentication Costs Protocols security Privacy Surveys Electronic voting computer science information security

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LDVNet: Lightweight and Detail-Aware Vision network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv

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arXiv 2025年

作者： Wang, Zhenglin Wu, Jialong Li, Pengfei Jiang, Yong Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Tongyi Lab Alibaba Group China

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Responding to News Sensitively in Stock Attention networks via Prompt-Adaptive Trimodal Model

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IEEE Transactions on Neural networks and Learning Systems 2025年第6期36卷 10099-10113页

作者： Haotian Liu Bowen Hu Yadong Zhou Yuxun Zhou Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley CA USA

Modern quantitative finance and portfolio-based investment hinge on multimedia news and historical price trends for stock movement prediction. However, prior studies overlook the long tail effect in the feature distribution of stocks, inevitably leading to biased attention and thus degrading the efficiency of utilizing news. To this end, we propose a prompt-adaptive trimodal model (PA-TMM) to overcome the biased stock attention networks and tail feature scarcity problem. In this model, sentiments automatically extracted from trimodal information serve as prompts reflecting the market’s collective mood for other entities, and the interactions among stocks are dynamically inferred for integrating both news- and price-induced movements. By leveraging the movement prompt adaptation (MPA) strategy, our model proactively adapts to the feature-imbalanced phenomenon and converges toward being responsive to the news sensitively. Extensive experiments conducted on real-world datasets consistently demonstrate not only the superiority of the proposed framework over various state-of-the-art baselines, but also its effectiveness, profitability, and robustness in Fintech. The code is accessible at https://***/lauht/PA-TMM .

关键词： Adaptation models Peer-to-peer computing Investment Heavily-tailed distribution Tail Stock markets Feature extraction Correlation Attention mechanisms Time series analysis

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