Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequ...Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.展开更多
Fuzz testing is a widely adopted technique for uncovering bugs and security vulnerabilities in embedded firmware.However,many embedded systems heavily rely on peripherals,rendering conventional fuzzing techniques inef...Fuzz testing is a widely adopted technique for uncovering bugs and security vulnerabilities in embedded firmware.However,many embedded systems heavily rely on peripherals,rendering conventional fuzzing techniques ineffective.When peripheral responses are missing or incorrect,fuzzing a firmware may crash or exit prematurely,significantly limiting code coverage.While prior re-hosting approaches have made progress in simulating Memory-Mapped Input/Output(MMIO)and interrupt-based peripherals,they either ignore Direct Memory Access(DMA)or handle it oversimplified.In this work,we present ADFEmu,a novel automated firmware re-hosting framework that enables effective fuzzing of DMA-enabled firmware.ADFEmu integrates concolic execution with large language models(LLMs)to semantically emulate DMA operations and synthesize peripheral input sequences intelligently.Specifically,it learns DMA transfer patterns from the firmware’s context and employs guided symbolic execution to explore deeper and more diverse execution paths.This approach allows firmware to operate stably without hardware dependencies while achieving higher fidelity in emulation.Evaluated on real-world embedded firmware samples,ADFEmu achieves a 100%re-hosting success rate,improves total execution path exploration by 5.31%,and triggers more crashes compared to the state-of-the-art.These results highlight ADFEmu’s effectiveness in overcoming long-standing limitations of DMA emulation and its potential to advance automated vulnerability discovery in peripheral-rich embedded environments.展开更多
Service robots are increasingly entering the home to provide domestic tasks for residents.However,when working in an open,dynamic,and unstructured home environment,service robots still face challenges such as low inte...Service robots are increasingly entering the home to provide domestic tasks for residents.However,when working in an open,dynamic,and unstructured home environment,service robots still face challenges such as low intelligence for task execution and poor long-term autonomy(LTA),which has limited their deployment.As the basis of robotic task execution,environment modeling has attracted significant attention.This integrates core technologies such as environment perception,understanding,and representation to accurately recognize environmental information.This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective.In particular,guided by the requirements of robots in performing domestic service tasks in the home environment,we systematically review the progress that has been made in task-executionoriented environmental modeling in four respects:1)localization,2)navigation,3)manipulation,and 4)LTA.Current challenges are discussed,and potential research opportunities are also highlighted.展开更多
With the rapid development of digital technologies such as big data,cloud computing,and the Internet of Things(loT),data security and privacy protection have become the core challenges facing modern computing systems....With the rapid development of digital technologies such as big data,cloud computing,and the Internet of Things(loT),data security and privacy protection have become the core challenges facing modern computing systems.Traditional security mechanisms are difficult to effectively deal with advanced adversarial attacks due to their reliance on a centralized trust model.In this context,the Trusted Execution Environment(TEE),as a hardware-enabled secure isolation technology,offers a potential solution to protect sensitive computations and data.This paper systematically discusses TEE's technical principle,application status,and future development trend.First,the underlying architecture of TEE and its core characteristics,including isolation,integrity,and confidentiality,are analyzed.Secondly,practical application cases of TEE in fields such as finance,the IoT,artificial intelligence,and privacy computing are studied.Finally,the future development direction of TEE is prospected.展开更多
Code obfuscation is a crucial technique for protecting software against reverse engineering and security attacks.Among various obfuscation methods,opaque predicates,which are recognized as flexible and promising,are w...Code obfuscation is a crucial technique for protecting software against reverse engineering and security attacks.Among various obfuscation methods,opaque predicates,which are recognized as flexible and promising,are widely used to increase control-flow complexity.However,traditional opaque predicates are increasingly vulnerable to Dynamic Symbolic Execution(DSE)attacks,which can efficiently identify and eliminate them.To address this issue,this paper proposes a novel approach for anti-DSE opaque predicates that effectively resists symbolic execution-based deobfuscation.Our method introduces two key techniques:single-way function opaque predicates,which leverage hash functions and logarithmic transformations to prevent constraint solvers from generating feasible inputs,and path-explosion opaque predicates,which generate an excessive number of execution paths,overwhelming symbolic execution engines.To evaluate the effectiveness of our approach,we implemented a prototype obfuscation tool and tested it against prominent symbolic execution engines.Experimental results demonstrate that our approach signifi-cantly increases resilience against symbolic execution attacks while maintaining acceptable performance overhead.This paper provides a robust and scalable obfuscation technique,contributing to the enhancement of software protection strategies in adversarial environments.展开更多
As mobile edge computing continues to develop,the demand for resource-intensive applications is steadily increasing,placing a significant strain on edge nodes.These nodes are normally subject to various constraints,fo...As mobile edge computing continues to develop,the demand for resource-intensive applications is steadily increasing,placing a significant strain on edge nodes.These nodes are normally subject to various constraints,for instance,limited processing capability,a few energy sources,and erratic availability being some of the common ones.Correspondingly,these problems require an effective task allocation algorithmto optimize the resources through continued high system performance and dependability in dynamic environments.This paper proposes an improved Particle Swarm Optimization technique,known as IPSO,for multi-objective optimization in edge computing to overcome these issues.To this end,the IPSO algorithm tries to make a trade-off between two important objectives,which are energy consumption minimization and task execution time reduction.Because of global optimal position mutation and dynamic adjustment to inertia weight,the proposed optimization algorithm can effectively distribute tasks among edge nodes.As a result,it reduces the execution time of tasks and energy consumption.In comparative assessments carried out by IPSO with benchmark methods such as Energy-aware Double-fitness Particle Swarm Optimization(EADPSO)and ICBA,IPSO provides better results than these algorithms.For the maximum task size,when compared with the benchmark methods,IPSO reduces the execution time by 17.1%and energy consumption by 31.58%.These results allow the conclusion that IPSO is an efficient and scalable technique for task allocation at the edge environment.It provides peak efficiency while handling scarce resources and variable workloads.展开更多
基金supported by the National Key Research and Development Program of China(2020YFB1005704).
文摘Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.
基金funded by the Science and Technology Project of State Grid Jiangsu Electric Power Company Ltd.,grant number J2024169.
文摘Fuzz testing is a widely adopted technique for uncovering bugs and security vulnerabilities in embedded firmware.However,many embedded systems heavily rely on peripherals,rendering conventional fuzzing techniques ineffective.When peripheral responses are missing or incorrect,fuzzing a firmware may crash or exit prematurely,significantly limiting code coverage.While prior re-hosting approaches have made progress in simulating Memory-Mapped Input/Output(MMIO)and interrupt-based peripherals,they either ignore Direct Memory Access(DMA)or handle it oversimplified.In this work,we present ADFEmu,a novel automated firmware re-hosting framework that enables effective fuzzing of DMA-enabled firmware.ADFEmu integrates concolic execution with large language models(LLMs)to semantically emulate DMA operations and synthesize peripheral input sequences intelligently.Specifically,it learns DMA transfer patterns from the firmware’s context and employs guided symbolic execution to explore deeper and more diverse execution paths.This approach allows firmware to operate stably without hardware dependencies while achieving higher fidelity in emulation.Evaluated on real-world embedded firmware samples,ADFEmu achieves a 100%re-hosting success rate,improves total execution path exploration by 5.31%,and triggers more crashes compared to the state-of-the-art.These results highlight ADFEmu’s effectiveness in overcoming long-standing limitations of DMA emulation and its potential to advance automated vulnerability discovery in peripheral-rich embedded environments.
基金supported in part by the National Natural Science Foundation of China(62203378,62203377,62073279)the Hebei Natural Science Foundation(F2024203036,F2024203115,F2025203101)+2 种基金the Science and Technology Program of Hebei(236Z2002G,236Z1603G)the Science Research Project of Hebei Education Department(BJK2024195)the National Research Foundation of Korea(NRF)Grant Funded by the Korea Government(Ministry of Science and ICT)(NRF-2020R1A2C1005449).
文摘Service robots are increasingly entering the home to provide domestic tasks for residents.However,when working in an open,dynamic,and unstructured home environment,service robots still face challenges such as low intelligence for task execution and poor long-term autonomy(LTA),which has limited their deployment.As the basis of robotic task execution,environment modeling has attracted significant attention.This integrates core technologies such as environment perception,understanding,and representation to accurately recognize environmental information.This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective.In particular,guided by the requirements of robots in performing domestic service tasks in the home environment,we systematically review the progress that has been made in task-executionoriented environmental modeling in four respects:1)localization,2)navigation,3)manipulation,and 4)LTA.Current challenges are discussed,and potential research opportunities are also highlighted.
文摘With the rapid development of digital technologies such as big data,cloud computing,and the Internet of Things(loT),data security and privacy protection have become the core challenges facing modern computing systems.Traditional security mechanisms are difficult to effectively deal with advanced adversarial attacks due to their reliance on a centralized trust model.In this context,the Trusted Execution Environment(TEE),as a hardware-enabled secure isolation technology,offers a potential solution to protect sensitive computations and data.This paper systematically discusses TEE's technical principle,application status,and future development trend.First,the underlying architecture of TEE and its core characteristics,including isolation,integrity,and confidentiality,are analyzed.Secondly,practical application cases of TEE in fields such as finance,the IoT,artificial intelligence,and privacy computing are studied.Finally,the future development direction of TEE is prospected.
基金supported byOpen Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(No.KLCS20240211)Henan Science and Technology Major Project No.241110210100.
文摘Code obfuscation is a crucial technique for protecting software against reverse engineering and security attacks.Among various obfuscation methods,opaque predicates,which are recognized as flexible and promising,are widely used to increase control-flow complexity.However,traditional opaque predicates are increasingly vulnerable to Dynamic Symbolic Execution(DSE)attacks,which can efficiently identify and eliminate them.To address this issue,this paper proposes a novel approach for anti-DSE opaque predicates that effectively resists symbolic execution-based deobfuscation.Our method introduces two key techniques:single-way function opaque predicates,which leverage hash functions and logarithmic transformations to prevent constraint solvers from generating feasible inputs,and path-explosion opaque predicates,which generate an excessive number of execution paths,overwhelming symbolic execution engines.To evaluate the effectiveness of our approach,we implemented a prototype obfuscation tool and tested it against prominent symbolic execution engines.Experimental results demonstrate that our approach signifi-cantly increases resilience against symbolic execution attacks while maintaining acceptable performance overhead.This paper provides a robust and scalable obfuscation technique,contributing to the enhancement of software protection strategies in adversarial environments.
基金supported by the University Putra Malaysia and the Ministry of Higher Education Malaysia under grantNumber:(FRGS/1/2023/ICT11/UPM/02/3).
文摘As mobile edge computing continues to develop,the demand for resource-intensive applications is steadily increasing,placing a significant strain on edge nodes.These nodes are normally subject to various constraints,for instance,limited processing capability,a few energy sources,and erratic availability being some of the common ones.Correspondingly,these problems require an effective task allocation algorithmto optimize the resources through continued high system performance and dependability in dynamic environments.This paper proposes an improved Particle Swarm Optimization technique,known as IPSO,for multi-objective optimization in edge computing to overcome these issues.To this end,the IPSO algorithm tries to make a trade-off between two important objectives,which are energy consumption minimization and task execution time reduction.Because of global optimal position mutation and dynamic adjustment to inertia weight,the proposed optimization algorithm can effectively distribute tasks among edge nodes.As a result,it reduces the execution time of tasks and energy consumption.In comparative assessments carried out by IPSO with benchmark methods such as Energy-aware Double-fitness Particle Swarm Optimization(EADPSO)and ICBA,IPSO provides better results than these algorithms.For the maximum task size,when compared with the benchmark methods,IPSO reduces the execution time by 17.1%and energy consumption by 31.58%.These results allow the conclusion that IPSO is an efficient and scalable technique for task allocation at the edge environment.It provides peak efficiency while handling scarce resources and variable workloads.
