Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, ...Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, poor generality. In order to solve these problems, this paper presents an improved ROP auto-constructor QExtd. Firstly, we design a Turing-complete language QExtdL and provide the basis of gadgets analysis. Secondly, we represent the MI instruction and realize precise process of side-effect instructions for improving the efficiency of automatic construction. At last, we establish a three-layer language conversion mechanism, making it convenient for users to construct ROP. Theoretical and experimental data show that the QExtd automatic construction method is much better than the ROPgadget based on syntax. In addition, the proposed method succeeds in constructing gadgets of ROP with the probability of 84% for programs whose sizes are more than 20 KB and whose directory is ＂/usr/bin＂ in Ubuntu, which proves that the construction capability improves significantly.展开更多
Providing knowledge graphs for materials science facilitates understanding of the key data such as materials structure,property,etc.and their relations.However,very little work has been devoted to it.Meanwhile,immedia...Providing knowledge graphs for materials science facilitates understanding of the key data such as materials structure,property,etc.and their relations.However,very little work has been devoted to it.Meanwhile,immediately applying machine learning to materials computation still suffers from the lack of data and costly acquiring.To tackle these problems,we propose literature-aid automatic entity and relation extraction by deliberatively designed matching rules,especially for copper-based composites.Next,we fuse the knowledge by calculating the semantics similarity.Finally,the materials knowledge graphs are constructed and visualized on the Neo4j graph database.The experimental results show a total of 6,154 entities and 15,561 pairs of relations are extracted on the 69,600 open-accessed documents of copper-based composites,with their precision and accuracy rates over 80%.Further,we exemplify the effectiveness by building materials structure-property-value meta-paths and analyzing their impacts.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA012902)
文摘Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, poor generality. In order to solve these problems, this paper presents an improved ROP auto-constructor QExtd. Firstly, we design a Turing-complete language QExtdL and provide the basis of gadgets analysis. Secondly, we represent the MI instruction and realize precise process of side-effect instructions for improving the efficiency of automatic construction. At last, we establish a three-layer language conversion mechanism, making it convenient for users to construct ROP. Theoretical and experimental data show that the QExtd automatic construction method is much better than the ROPgadget based on syntax. In addition, the proposed method succeeds in constructing gadgets of ROP with the probability of 84% for programs whose sizes are more than 20 KB and whose directory is ＂/usr/bin＂ in Ubuntu, which proves that the construction capability improves significantly.
基金partially supported by the Natural Science Foundation of China(62062046)
文摘Providing knowledge graphs for materials science facilitates understanding of the key data such as materials structure,property,etc.and their relations.However,very little work has been devoted to it.Meanwhile,immediately applying machine learning to materials computation still suffers from the lack of data and costly acquiring.To tackle these problems,we propose literature-aid automatic entity and relation extraction by deliberatively designed matching rules,especially for copper-based composites.Next,we fuse the knowledge by calculating the semantics similarity.Finally,the materials knowledge graphs are constructed and visualized on the Neo4j graph database.The experimental results show a total of 6,154 entities and 15,561 pairs of relations are extracted on the 69,600 open-accessed documents of copper-based composites,with their precision and accuracy rates over 80%.Further,we exemplify the effectiveness by building materials structure-property-value meta-paths and analyzing their impacts.
