The civil aviation industry has made rapid development with the continuous growth of China＇s economy. At the same time ,the airline companies buy more and more civil aircrafts.This phenomenon will lead to a big flyin...The civil aviation industry has made rapid development with the continuous growth of China＇s economy. At the same time ,the airline companies buy more and more civil aircrafts.This phenomenon will lead to a big flying density ,which will make airport management become more complex.This paper first introduces the characteristic of security programs before flight and the single code network plan, and then put the security programs before flight into the single code network plan to find out the key route and calculate the guarantee period. Finally, we analyse the programs according to the guarantee period and critical route.It not only provides a support for administrator, but also we present a reasonable methods to reduce the guarantee period.展开更多
In this paper the calculation formulae of optimum beginning time-inter-val and optimum finishing time-interval between every two adjacent operations forthe most general current production network are derived.Two examp...In this paper the calculation formulae of optimum beginning time-inter-val and optimum finishing time-interval between every two adjacent operations forthe most general current production network are derived.Two examples show thatthese formulae are very convenient for calculation.展开更多
Network protocol software is usually characterized by complicated functions and a vast state space.In this type of program,a massive number of stateful variables that are used to represent the evolution of the states ...Network protocol software is usually characterized by complicated functions and a vast state space.In this type of program,a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potentialflaws caused by violations of protocol specification requirements and program logic.Discovering such variables is significant in discovering and exploiting vulnerabilities in protocol software,and still needs massive manual verifications.In this paper,we propose a novel method that could automatically discover the use of stateful variables in network protocol software.The core idea is that a stateful variable features information of the communication entities and the software states,so it will exist in the form of a global or static variable during program execution.Based on recording and replaying a protocol program’s execution,varieties of variables in the life cycle can be tracked with the technique of dynamic instrument.We draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful characteristics.We also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in Pro FuzzBench and two complex real-world software programs.With the help of available open-source code,the evaluation results show that the average true positive rate(TPR)can reach 82%and the average precision can be approximately up to 96%.展开更多
Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics,a tedious and time consuming task for human analysts.In order to improve automation and scalability,we propose...Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics,a tedious and time consuming task for human analysts.In order to improve automation and scalability,we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks.We introduce Bin2vec,a new approach leveraging Graph Convolutional Networks(GCN)along with computational program graphs in order to learn a high dimensional representation of binary executable programs.We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks–functional algorithm classification and vulnerability discovery.We compare the proposed approach to our own strong baseline as well as published results,and demonstrate improvement over state-of-the-art methods for both tasks.We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task,and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task.We set a new state-of-the-art result by reducing the classification error by 40%compared to the source-code based inst2vec approach,while working on binary code.For almost every vulnerability class in our dataset,our prediction accuracy is over 80%(and over 90%in multiple classes).展开更多
文摘The civil aviation industry has made rapid development with the continuous growth of China＇s economy. At the same time ,the airline companies buy more and more civil aircrafts.This phenomenon will lead to a big flying density ,which will make airport management become more complex.This paper first introduces the characteristic of security programs before flight and the single code network plan, and then put the security programs before flight into the single code network plan to find out the key route and calculate the guarantee period. Finally, we analyse the programs according to the guarantee period and critical route.It not only provides a support for administrator, but also we present a reasonable methods to reduce the guarantee period.
文摘In this paper the calculation formulae of optimum beginning time-inter-val and optimum finishing time-interval between every two adjacent operations forthe most general current production network are derived.Two examples show thatthese formulae are very convenient for calculation.
基金Project supported by the National Natural Science Foundation of China(Nos.61902416 and 61902412)the Natural Science Foundation of Hunan Province,China(No.2019JJ50729)。
文摘Network protocol software is usually characterized by complicated functions and a vast state space.In this type of program,a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potentialflaws caused by violations of protocol specification requirements and program logic.Discovering such variables is significant in discovering and exploiting vulnerabilities in protocol software,and still needs massive manual verifications.In this paper,we propose a novel method that could automatically discover the use of stateful variables in network protocol software.The core idea is that a stateful variable features information of the communication entities and the software states,so it will exist in the form of a global or static variable during program execution.Based on recording and replaying a protocol program’s execution,varieties of variables in the life cycle can be tracked with the technique of dynamic instrument.We draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful characteristics.We also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in Pro FuzzBench and two complex real-world software programs.With the help of available open-source code,the evaluation results show that the average true positive rate(TPR)can reach 82%and the average precision can be approximately up to 96%.
文摘Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics,a tedious and time consuming task for human analysts.In order to improve automation and scalability,we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks.We introduce Bin2vec,a new approach leveraging Graph Convolutional Networks(GCN)along with computational program graphs in order to learn a high dimensional representation of binary executable programs.We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks–functional algorithm classification and vulnerability discovery.We compare the proposed approach to our own strong baseline as well as published results,and demonstrate improvement over state-of-the-art methods for both tasks.We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task,and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task.We set a new state-of-the-art result by reducing the classification error by 40%compared to the source-code based inst2vec approach,while working on binary code.For almost every vulnerability class in our dataset,our prediction accuracy is over 80%(and over 90%in multiple classes).
