In the wake of the research community gaining deep understanding about control-hijacking attacks,data-oriented attacks have emerged.Among data-oriented attacks,data structure manipulation attack(DSMA)is a major catego...In the wake of the research community gaining deep understanding about control-hijacking attacks,data-oriented attacks have emerged.Among data-oriented attacks,data structure manipulation attack(DSMA)is a major category.Pioneering research was conducted and shows that DSMA is able to circumvent the most effective defenses against control-hijacking attacks-DEP,ASLR and CFI.Up to this day,only two defense techniques have demonstrated their effectiveness:Data Flow Integrity(DFI)and Data Structure Layout Randomization(DSLR).However,DFI has high performance overhead,and dynamic DSLR has two main limitations.L-1:Randomizing a large set of data structures will significantly affect the performance.L-2:To be practical,only a fixed sub-set of data structures are randomized.In the case that the data structures targeted by an attack are not covered,dynamic DSLR is essentially noneffective.To address these two limitations,we propose a novel technique,feedback-control-based adaptive DSLR and build a system named SALADSPlus.SALADSPlus seeks to optimize the trade-off between security and cost through feedback control.Using a novel feedback-control-based adaptive algorithm extended from the Upper Confidence Bound(UCB)algorithm,the defender(controller)uses the feedbacks(cost-effectiveness)from previous randomization cycles to adaptively choose the set of data structures to randomize(the next action).Different from dynamic DSLR,the set of randomized data structures are adaptively changed based on the feedbacks.To obtain the feedbacks,SALADSPlus inserts canary in each data structure at the time of compilation.We have implemented SALADSPlus based on gcc-4.5.0.Experimental results show that the runtime overheads are 1.8%,3.7%,and 5.3% when the randomization cycles are selected as 10s,5s,and 1s respectively.展开更多
Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimizat...Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimization is not widely used. To overcome these drawbacks, a performance monitoring unit （PMU） sampling method was developed with much less overhead and better portability and usability. An algorithm was developed to correct incomplete and inaccurate PMU sampling. With the corrected PMU feedback, a structure data-layout optimizer achieved a 45.1% performance improvement compared to a design without data-layout optimization, which is 97.6% of the performance improvement achieved with instrumented feedback. Calculation of the PMU feedback increased the execution time by 12.3%, compared to the overhead for the instrumented feedback of 341.5%. Tests show that the PMU feedback is efficient and effective for structure data-layout optimization.展开更多
Structure Data Layout Optimization （SDLO） is a prevailing compiler optimization technique to improve cache efficiency. Structure transformation is a critical step for SDLO. Diversity of transformation methods and ex...Structure Data Layout Optimization （SDLO） is a prevailing compiler optimization technique to improve cache efficiency. Structure transformation is a critical step for SDLO. Diversity of transformation methods and existence of complex data types are major challenges for structure transformation. We have designed and implemented STrans, a well-defined system which provides controllable and comprehensive functionality on structure transformation. Compared to known systems, it has less limitation on data types for transformation. In this paper we give formal definition of the approach STrans transforms data types. We have also designed Transformation Specification Language, a mini language to configure how to transform structures, which can be either manually tuned or generated by compiler. STrans supports three kinds of transformation methods, i.e., splitting, peeling, and pool-splitting, and works well on different combinations of compound data types. STrans is the transformation system used in ASLOP and is well tested for all benchmarks for ASLOR展开更多
基金supported by ARO W911NF-13-1-0421(MURI)NSF CNS-1422594NSF CNS-1505664.
文摘In the wake of the research community gaining deep understanding about control-hijacking attacks,data-oriented attacks have emerged.Among data-oriented attacks,data structure manipulation attack(DSMA)is a major category.Pioneering research was conducted and shows that DSMA is able to circumvent the most effective defenses against control-hijacking attacks-DEP,ASLR and CFI.Up to this day,only two defense techniques have demonstrated their effectiveness:Data Flow Integrity(DFI)and Data Structure Layout Randomization(DSLR).However,DFI has high performance overhead,and dynamic DSLR has two main limitations.L-1:Randomizing a large set of data structures will significantly affect the performance.L-2:To be practical,only a fixed sub-set of data structures are randomized.In the case that the data structures targeted by an attack are not covered,dynamic DSLR is essentially noneffective.To address these two limitations,we propose a novel technique,feedback-control-based adaptive DSLR and build a system named SALADSPlus.SALADSPlus seeks to optimize the trade-off between security and cost through feedback control.Using a novel feedback-control-based adaptive algorithm extended from the Upper Confidence Bound(UCB)algorithm,the defender(controller)uses the feedbacks(cost-effectiveness)from previous randomization cycles to adaptively choose the set of data structures to randomize(the next action).Different from dynamic DSLR,the set of randomized data structures are adaptively changed based on the feedbacks.To obtain the feedbacks,SALADSPlus inserts canary in each data structure at the time of compilation.We have implemented SALADSPlus based on gcc-4.5.0.Experimental results show that the runtime overheads are 1.8%,3.7%,and 5.3% when the randomization cycles are selected as 10s,5s,and 1s respectively.
文摘Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimization is not widely used. To overcome these drawbacks, a performance monitoring unit （PMU） sampling method was developed with much less overhead and better portability and usability. An algorithm was developed to correct incomplete and inaccurate PMU sampling. With the corrected PMU feedback, a structure data-layout optimizer achieved a 45.1% performance improvement compared to a design without data-layout optimization, which is 97.6% of the performance improvement achieved with instrumented feedback. Calculation of the PMU feedback increased the execution time by 12.3%, compared to the overhead for the instrumented feedback of 341.5%. Tests show that the PMU feedback is efficient and effective for structure data-layout optimization.
基金supported by the National Natural Science Foundation of China(No.61133006)the National High-Tech Research and Development(863)Program of China(No.2012AA010901)
文摘Structure Data Layout Optimization （SDLO） is a prevailing compiler optimization technique to improve cache efficiency. Structure transformation is a critical step for SDLO. Diversity of transformation methods and existence of complex data types are major challenges for structure transformation. We have designed and implemented STrans, a well-defined system which provides controllable and comprehensive functionality on structure transformation. Compared to known systems, it has less limitation on data types for transformation. In this paper we give formal definition of the approach STrans transforms data types. We have also designed Transformation Specification Language, a mini language to configure how to transform structures, which can be either manually tuned or generated by compiler. STrans supports three kinds of transformation methods, i.e., splitting, peeling, and pool-splitting, and works well on different combinations of compound data types. STrans is the transformation system used in ASLOP and is well tested for all benchmarks for ASLOR
