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A buffer overflow detection and defense method based on RiSC-V instruction set extension

Cybersecurity 2024年第2期7卷 28-43页

作者： Chang Liu Yan-Jun Wu Jing-Zheng Wu Chen Zhao intelligent software Research Center Institute of SoftwareChinese Academy of SciencesBejingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingChina

Buffer overflow poses a serious threat to the memory security of modern operating *** overwrites the con-tents of other memory areas by breaking through the buffer capacity limit,destroys the system execution environ-ment,and provides implementation space for various system attacks such as program control flow *** makes it a wide range of harms.A variety of security technologies have been proposed to deal with system security problems including buffer *** example,No eXecute(NX for short)is a memory management technology commonly used in Harvard *** can refuse the execution of code which residing in a specific memory,and can effectively suppress the abnormal impact of buffer overflow on control ***,in recent years,it has also been used in the field of system security,deriving a series of solutions based on NX technology,such as ExecShield,DEP,StackGuard,***,these security solutions often rely too much on the processor archi-tecture so that the protection coverage is insufficient and the accuracy is *** in the emerging system architecture field represented by RiSC-V,there is still a lack of effective solutions for buffer overflow *** the continuous rapid development of the system architecture,it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security ***,we propose BOP,A new system memory security design method based on RISC-V extended instructions,to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in *** to this method,NX technology can be combined with program control flow analysis,and Nx bit mechanism can be used to manage the executability of memory space,so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system

关键词： RISc-V Operating system security Buffer overflow Control flow hijacking Nx bit Xibop

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 769-786页

作者： Xiaojun Pu Jiaqi Zhu Yunkun Wu Chang Leng Zitong Bo Hongan Wang institute of software Chinese Academy of Sciences BeijingChina Ecovacs Robotics Co. Ltd.SuzhouJiangsuChina Automotive software Innovation Center ChongqingChina

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 ***,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal ***,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction *** DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency *** dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained ***,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained *** performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute *** results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of *** spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various *** conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

关键词： adaptive COVID-19 forecasting dynamic intervention spatio-temporal graph neural networks

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A survey on facial image deblurring

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Computational Visual Media 2024年第1期10卷 3-25页

作者： Bingnan Wang Fanjiang Xu Quan Zheng institute of software Chinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

When a facial image is blurred,it significantly affects high-level vision tasks such as face *** purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve the recognition accuracy,***,general deblurring methods do not perform well on facial ***,some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial *** this paper,we survey and summarize recently published methods for facial image deblurring,most of which are based on deep ***,we provide a brief introduction to the modeling of image ***,we summarize face deblurring methods into two categories:model-based methods and deep learning-based ***,we summarize the datasets,loss functions,and performance evaluation metrics commonly used in the neural network training *** show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based ***,we discuss the current challenges and possible future research directions.

关键词： facial image deblurring model-based deep learning-based semantic or structural prior

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TULAM:trajectory-user linking via attention mechanism

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Science China(Information Sciences) 2024年第1期67卷 51-68页

作者： Hao LI Shuyu CAO Yaqing CHEN Min ZHANG Dengguo FENG Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences University of Chinese Academy of Sciences

Recently,the application of location-based services(LBS) has become a prevalent means to provide convenience in customers’ everyday ***,because massive volumes of location information are collected by LBS applications,users may suffer from serious privacy *** studies have shown that the identities of users can be inferred from historical anonymous trajectories,which is formulated as the trajectory-user linking(TUL) *** some recurrent neural network(RNN)-based models have been proposed to capture implicit movement patterns among trajectories to improve TUL performance,they cannot learn the sequential and contextual semantics within any individual trajectory completely,leaving the advantages of RNNs *** therefore propose an RNN model with an attention mechanism called TULAM to improve the accuracy of the TUL *** learns sequential relationships within individual trajectories via RNN and captures contextual semantics from trajectories via a multi-head attention ***,we propose a novel location encoding method called approximate one-hot to solve the corpus shortage problem of trajectory *** were conducted on real datasets from the Gowalla and Foursquare LBS *** experimental results indicate that TULAM is a practical solution that achieves significant improvements over existing methods with satisfactory model complexity and convergence.

关键词： information security data security and privacy trajectory-user linking deep learning recurrent neural network attention mechanism

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Categorizing methods for integrating machine learning with executable specifications

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Science China(Information Sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Partially-hiding functional encryption for degree-2 polynomials with fine-grained access control

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Frontiers of Computer Science 2024年第3期18卷 231-233页

作者： Haifeng QIAN Cheng LIN Qiaohan CHU Jie CHEN Shanghai Key Laboratory of Trustworthy Computing Software Engineering InstituteEast China Normal UniversityShanghai 200062China

1 Introduction Boneh et al.[1]first systematically introduce the concept of Functional Encryption(FE)which overcomes all-or-nothing limitation of traditional public key ***,FE hides a potential problem:even if the secret key is obtained legitimately,once it is released,it can be used *** leads to that the secret key may leak information *** deal with this problem and make FE more practical,Abdalla et al.[2]recently constructed inner-product FE(IPFE)with fine-grained access control achieving strong security guarantees under standard assumptions.

关键词： grain Encryption encryption

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Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

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Petroleum Science 2024年第4期21卷 2345-2355页

作者： Nai-Hao Liu Yu-Xin Zhang Yang Yang Rong-Chang Liu Jing-Huai Gao Nan Zhang School of Information and Communications Engineering Xi'an Jiaotong University School of software Engineering Xi'an Jiaotong University PetroChina Research institute of Petroleum Exploration and Development (RIPED) CNPC Research institute of Exploration and Development Yumen Oilfield Company CNPC

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic ***, it suffers from several inevitable limitations, such as the restricted time-frequency resolution,the difficulty in selecting parameters, and the low computational efficiency. Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis. The sparse S transform network(SSTNet) is first built to map the relationship between synthetic traces and sparse S transform spectra, which can be easily pre-trained by using synthetic traces and training labels. Next, we introduce knowledge distillation(KD) based transfer learning to re-train SSTNet by using a field data set without training labels, which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way, we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels. To test the availability of the suggested KD-SSTNet, we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

关键词： S transform Deep learning Knowledge distillation Transfer learning Seismic attenuation delineation

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Exploring the effect of fingertip aero-haptic feedforward cues in directing eyes-free target acquisition in VR

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虚拟现实与智能硬件（中英文） 2024年第2期6卷 113-131页

作者： Xiaofei REN Jian HE Teng HAN Songxian LIU Mengfei LV Rui ZHOU Faculty of Information Technology Beijing University of TechnologyBeijing 100124China institute of software Chinese Academy of SciencesBeijing 100190China Beijing Engineering Research Center for IoT software and Systems Beijing University of TechnologyBeijing 100124China

Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is *** haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical *** This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial *** wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the *** The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning *** The haptic feedforward effect holds great practical promise in eyeless design for virtual *** aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.

关键词： Haptic Feedforward Virtual reality Aero-haptic

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An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3535-3563页

作者： Younghoon Ban Myeonghyun Kim Haehyun Cho School of software Soongsil UniversitySeoul06978Korea

Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware *** the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based *** antivirus systems,in particular,have been identified as vulnerable to such *** paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware *** attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack *** directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack *** a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based ***,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality *** thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware ***,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense *** several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation *** conclusion,our defense model alleviates the limitation of the mos

关键词： Malware classification machine learning adversarial examples evasion attack cybersecurity

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