In recent years many security attacks occur when malicious codes abuse in-process memory resources.Due to the increasing complexity,an application program may call third-party code which cannot be controlled by progra...In recent years many security attacks occur when malicious codes abuse in-process memory resources.Due to the increasing complexity,an application program may call third-party code which cannot be controlled by programmers but may contain security vulnerabilities.As a result,the users have the risk of suffering information leakage and control flow hijacking.However,current solutions like Intel memory protection extensions(MPX)severely degrade performance,while other approaches like Intel memory protection keys(MPK)lack flexibility in dividing security domains.In this paper,we propose IMPULP,an effective and efficient hardware approach for in-process memory protection.The rationale of IMPULP is user-level partitioning that user code segments are divided into different security domains according to their instruction addresses,and accessible memory spaces are specified dynamically for each domain via a set of boundary registers.Each instruction related to memory access will be checked according to its security domain and the corresponding boundaries,and illegal in-process memory access of untrusted code segments will be prevented.IMPULP can be leveraged to prevent a wide range of in-process memory abuse attacks,such as buffer overflows and memory leakages.For verification,an FPGA prototype based on RISC-V instruction set architecture has been developed.We present eight tests to verify the effectiveness of IMPULP,including five memory protection function tests,a test to defense typical buffer overflow,a test to defense famous memory leakage attack named Heartbleed,and a test for security benchmark.We execute the SPEC CPU2006 benchmark programs to evaluate the efficiency of IMPULP.The performance overhead of IMPULP is less than 0.2%runtime on average,which is negligible.Moreover,the resource overhead is less than 5.5%for hardware modification of IMPULP.展开更多
Users express their emotions in online social networks （OSNs）. Studying emotions is important for understanding user behaviors. Existing methods in emotion prediction mainly use personal emotion and friend emotion t...Users express their emotions in online social networks （OSNs）. Studying emotions is important for understanding user behaviors. Existing methods in emotion prediction mainly use personal emotion and friend emotion to predict target emotion. In this study, public sentiment is introduced to denote the sentiment of the majority in the network. Public conformity is calculated to measure the degree of a user conforming to the public sentiment. According to the public conformity, users are categorized into three classes： Approvers, independents, and starters. A user-level model for emotion prediction is proposed to predict target emotions of different classes of users, taking into account of the public sentiment, individual sentiment, friend sentiment and pseudo-friend sentiment. Relations between the public conformity and the structure of the network are studied. Experiments conducted on Sina Weibo show that the proposed model could achieve performance improvements to some existing methods in most cases.展开更多
提出了一种基于双层模型预测控制(model predictive control,MPC)的建筑与社区综合能源系统(integrated community energy system,ICES)主从博弈协调优化方法。首先,采用热阻-热容网络对建筑用户采暖负荷的热动态特性进行了建模。其次,...提出了一种基于双层模型预测控制(model predictive control,MPC)的建筑与社区综合能源系统(integrated community energy system,ICES)主从博弈协调优化方法。首先,采用热阻-热容网络对建筑用户采暖负荷的热动态特性进行了建模。其次,提出了基于双层MPC的建筑与ICES协调优化模型,在兼顾ICES运营商和建筑用户的差异化利益诉求的基础上,进一步考虑了在协调优化过程中面临的风电和光伏出力、ICES运营商向上级能源系统购买能源的价格、室外温度和光照强度等预测数据的不确定性。最后,通过算例验证了所提方法可合理平衡ICES和建筑用户的差异化利益诉求并有效应对协调优化中所面临的不确定性。展开更多
基金This work was supported by the National Key Research and Development Plan of China under Grant No.2016YFB1000200the National Natural Science Foundation of China under Grant No.61772497the State Key Laboratory of Computer Architecture Foundation under Grant Nos.CARCH4405 and CARCH2601.
文摘In recent years many security attacks occur when malicious codes abuse in-process memory resources.Due to the increasing complexity,an application program may call third-party code which cannot be controlled by programmers but may contain security vulnerabilities.As a result,the users have the risk of suffering information leakage and control flow hijacking.However,current solutions like Intel memory protection extensions(MPX)severely degrade performance,while other approaches like Intel memory protection keys(MPK)lack flexibility in dividing security domains.In this paper,we propose IMPULP,an effective and efficient hardware approach for in-process memory protection.The rationale of IMPULP is user-level partitioning that user code segments are divided into different security domains according to their instruction addresses,and accessible memory spaces are specified dynamically for each domain via a set of boundary registers.Each instruction related to memory access will be checked according to its security domain and the corresponding boundaries,and illegal in-process memory access of untrusted code segments will be prevented.IMPULP can be leveraged to prevent a wide range of in-process memory abuse attacks,such as buffer overflows and memory leakages.For verification,an FPGA prototype based on RISC-V instruction set architecture has been developed.We present eight tests to verify the effectiveness of IMPULP,including five memory protection function tests,a test to defense typical buffer overflow,a test to defense famous memory leakage attack named Heartbleed,and a test for security benchmark.We execute the SPEC CPU2006 benchmark programs to evaluate the efficiency of IMPULP.The performance overhead of IMPULP is less than 0.2%runtime on average,which is negligible.Moreover,the resource overhead is less than 5.5%for hardware modification of IMPULP.
基金supported in part by National Key Basic Research and Department (973) Program of China (2013CB329606)Natural Science Foundation of China (61402045)
文摘Users express their emotions in online social networks （OSNs）. Studying emotions is important for understanding user behaviors. Existing methods in emotion prediction mainly use personal emotion and friend emotion to predict target emotion. In this study, public sentiment is introduced to denote the sentiment of the majority in the network. Public conformity is calculated to measure the degree of a user conforming to the public sentiment. According to the public conformity, users are categorized into three classes： Approvers, independents, and starters. A user-level model for emotion prediction is proposed to predict target emotions of different classes of users, taking into account of the public sentiment, individual sentiment, friend sentiment and pseudo-friend sentiment. Relations between the public conformity and the structure of the network are studied. Experiments conducted on Sina Weibo show that the proposed model could achieve performance improvements to some existing methods in most cases.
文摘提出了一种基于双层模型预测控制(model predictive control,MPC)的建筑与社区综合能源系统(integrated community energy system,ICES)主从博弈协调优化方法。首先,采用热阻-热容网络对建筑用户采暖负荷的热动态特性进行了建模。其次,提出了基于双层MPC的建筑与ICES协调优化模型,在兼顾ICES运营商和建筑用户的差异化利益诉求的基础上,进一步考虑了在协调优化过程中面临的风电和光伏出力、ICES运营商向上级能源系统购买能源的价格、室外温度和光照强度等预测数据的不确定性。最后,通过算例验证了所提方法可合理平衡ICES和建筑用户的差异化利益诉求并有效应对协调优化中所面临的不确定性。
