Software vulnerabilities are the root cause of various information security incidents while dynamic taint analysis is an emerging program analysis technique. In this paper, to maximize the use of the technique to dete...Software vulnerabilities are the root cause of various information security incidents while dynamic taint analysis is an emerging program analysis technique. In this paper, to maximize the use of the technique to detect software vulnerabilities, we present SwordDTA, a tool that can perform dynamic taint analysis for binaries. This tool is flexible and extensible that it can work with commodity software and hardware. It can be used to detect software vulnerabilities with vulnerability modeling and taint check. We evaluate it with a number of commonly used real-world applications. The experimental results show that SwordDTA is capable of detecting at least four kinds of softavare vulnerabilities including buffer overflow, integer overflow, division by zero and use-after-free, and is applicable for a wide range of software.展开更多
With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks ...With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.展开更多
Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid devel...Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.展开更多
It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed...It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed to realize the description of vulnerability attribute and the construction of a vulnerability model. A vulnerability model based on chemical abstract machine（CHAM） is constructed to realize the CHAM description of vulnerability model, and the framework of vulnerability model is also discussed. Case study is carried out to verify the feasibility and effectiveness of the proposed model. In addition, a prototype system is also designed and implemented based on the proposed vulnerability model. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA012902)the“HGJ”National Major Technological Projects(2013ZX01045-004)
文摘Software vulnerabilities are the root cause of various information security incidents while dynamic taint analysis is an emerging program analysis technique. In this paper, to maximize the use of the technique to detect software vulnerabilities, we present SwordDTA, a tool that can perform dynamic taint analysis for binaries. This tool is flexible and extensible that it can work with commodity software and hardware. It can be used to detect software vulnerabilities with vulnerability modeling and taint check. We evaluate it with a number of commonly used real-world applications. The experimental results show that SwordDTA is capable of detecting at least four kinds of softavare vulnerabilities including buffer overflow, integer overflow, division by zero and use-after-free, and is applicable for a wide range of software.
基金supported by a National Research Foundation of Korea (NRF)grant funded by the Ministry of Science and ICT (MSIT) (No.2020R1F1A1061107)the Korea Institute for Advancement of Technology (KIAT)grant funded by the Korean Government (MOTIE) (P0008703,The Competency Development Program for Industry Specialists)the MSIT under the ICAN (ICT Challenge and Advanced Network of HRD)program (No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning and Evaluation (IITP).
文摘With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.
基金partly supported by National Natural Science Foundation of China (NSFC grant numbers: 61202110 and 61502205)the project of Jiangsu provincial Six Talent Peaks (Grant numbers: XYDXXJS-016)
文摘Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.
基金Supported by the National Natural Science Foundation of China(61202110 and 61502205)the Project of Jiangsu Provincial Six Talent Peaks(XYDXXJS-016)
文摘It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed to realize the description of vulnerability attribute and the construction of a vulnerability model. A vulnerability model based on chemical abstract machine（CHAM） is constructed to realize the CHAM description of vulnerability model, and the framework of vulnerability model is also discussed. Case study is carried out to verify the feasibility and effectiveness of the proposed model. In addition, a prototype system is also designed and implemented based on the proposed vulnerability model. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.
