• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.147-155
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• 2010

It has been widely addressed that static analysis techniques can play important role in identifying potential security vulnerability reside in source code. This paper proposes the design and application of security metrics that use both vulnerability information extracted from the static analysis, and significant factors of information that software handles. The security metrics are useful for both developers and evaluators in that the metrics help them identity source code vulnerability in early stage of development. By effectively utilizing the security metrics, evaluators can check the level of source code security, and confirm the final code depending on the characteristics of the source code and the security level of information required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1689-1705
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• 2023

To deal with the potential XSS vulnerabilities in the source code of the power communication network, an XSS vulnerability detection method combining the static analysis method with the dynamic testing method is proposed. The static analysis method aims to analyze the structure and content of the source code. We construct a set of feature expressions to match malignant content and set a "variable conversion" method to analyze the data flow of the code that implements interactive functions. The static analysis method explores the vulnerabilities existing in the source code structure and code content. Dynamic testing aims to simulate network attacks to reflect whether there are vulnerabilities in web pages. We construct many attack vectors and implemented the test in the Selenium tool. Due to the combination of the two analysis methods, XSS vulnerability discovery research could be conducted from two aspects: "white-box testing" and "black-box testing". Tests show that this method can effectively detect XSS vulnerabilities in the source code of the power communication network.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.45-52
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• 2020

Traditional methods of detecting security vulnerabilities in source-code require a lot of time and effort. If there is good data, the issue could be solved by using the data with machine learning. Thus, this paper proposes a source-code vulnerability detection method based on machine learning. Our method employs the code2vec model that has been used to propose the names of methods, and uses as a data set, Juliet Test Suite that is a collection of common security vulnerabilities. The evaluation shows that our method has high precision of 97.3% and recall rates of 98.6%. And the result of detecting vulnerabilities in open source project shows hopeful potential. In addition, it is expected that further progress can be made through studies covering with vulnerabilities and languages not addressed here.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.17-21
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• 2022

In this study, we wanted to examine the new vulnerability 'Dirty Pipe' that is founded in Linux kernel. how it's exploited and what is the limitation, where it's existed, and overcome techniques and analysis of the Linux kernel package. The study of the method used the hmark[1] program to check the vulnerabilities. Hmark is a whitebox testing tool that helps to analyze the vulnerability based on static whitebox testing and automated verification. For this purpose of our study, we analyzed Linux kernel code that is downloaded from an open-source website. Then by analyzing the hmark tool results, we identified in which file of the kernel it exists, cvss level, statistically depicted vulnerabilities on graph which is easy to understand. Furthermore, we will talk about some software we can use to analyze a vulnerability and how hmark software works. In the case of the Dirty Pipe vulnerability in Linux allows non-privileged users to execute malicious code capable of a host of destructive actions including installing backdoors into the system, injecting code into scripts, altering binaries used by elevated programs, and creating unauthorized user profiles. This bug is being tracked as CVE-2022-0847 and has been termed "Dirty Pipe"[2] since it bears a close resemblance to Dirty Cow[3], and easily exploitable Linux vulnerability from 2016 which granted a bad actor an identical level of privileges and powers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1225-1236
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• 2020

Fuzzing is an automated software testing methodology that dynamically tests the security of software by inputting randomly generated input values outside of the expected range. KISA is releasing open source for standard cryptographic algorithms, and many crypto module developers are developing crypto modules using this source code. If there is a vulnerability in the open source code, the cryptographic library referring to it has a potential vulnerability, which may lead to a security accident that causes enormous losses in the future. Therefore, in this study, an appropriate security policy was established to verify the safety of block cipher source codes such as SEED, HIGHT, and ARIA, and the safety was verified using differential fuzzing. Finally, a total of 45 vulnerabilities were found in the memory bug items and error handling items, and a vulnerability improvement plan to solve them is proposed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.687-699
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• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1139-1150
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• 2022

With the rapid development of SW Industry, softwares are everywhere in our daily life. The number of vulnerabilities are also increasing with a large amount of newly developed code. Vulnerabilities can be exploited by hackers, resulting the disclosure of privacy and threats to the safety of property and life. In particular, since the large numbers of increasing code, manually analyzed by expert is not enough anymore. Machine learning has shown high performance in object identification or classification task. Vulnerability detection is also suitable for machine learning, as a reuslt, many studies tried to use RNN-based model to detect vulnerability. However, the RNN model is also has limitation that as the code is longer, the earlier can not be learned well. In this paper, we proposed a novel method which applied BERT to detect vulnerability. The accuracy was 97.5%, which increased by 1.5%, and the efficiency also increased by 69% than Vuldeepecker.

• Journal of KIISE
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• v.44 no.10
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• pp.1019-1025
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• 2017

Binary similarity analysis is used in vulnerability analysis, malicious code analysis, and plagiarism detection. Proving that a function is equal to a well-known safe functions of different versions through similarity analysis can help to improve the efficiency of the binary code analysis of malicious behavior as well as the efficiency of vulnerability analysis. However, few studies have been carried out on similarity analysis of the same function of different versions. In this paper, we analyze the similarity of function units through various methods based on extractable function information from binary code, and find a way to analyze efficiently with less time. In particular, we perform a comparative analysis of the different versions of the OpenSSL library to determine the way in which similar functions are detected even when the versions differ.

• Journal of Software Engineering Society
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• v.28 no.1
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• pp.13-22
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• 2019

To enhance effective and efficient applications of automated security vulnerability checkers in highly competitive and fast-evolving IT industry, this paper studies a comprehensive set of security bug checkers in open-source static analysis frameworks and how they can be utilized for source code inspections according to the security vulnerability inspection guidelines by MOIS. This paper clarifies the relationship be tween all 42 inspection criteria in the MOIS guideline and total 323 security bug checkers in 4 popular open-source static analysis frameworks for Java web applications. Based on the result, this paper also discuss the current challenges and issues in the MOIS guideline, the comparison among the four security bug checker frameworks, and also the ideas to improve the security inspection methodologies using the MOIS guideline and open-source static security bug checkers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.701-713
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• 2021

Race Condition is a vulnerability in which two or more processes input or manipulate a common resource at the same time, resulting in unintended results. This vulnerability can lead to problems such as denial of service, elevation of privilege. When a vulnerability occurs in software, the relevant information is documented, but often the cause of the vulnerability or the source code is not disclosed. In this case, analysis at the binary level is necessary to detect the vulnerability. This paper aims to detect the Time-Of-Check Time-Of-Use (TOCTOU) Race Condition vulnerability of UNIX kernel-based File System at the binary level. So far, various detection techniques of static/dynamic analysis techniques have been studied for the vulnerability. Existing vulnerability detection tools using static analysis detect through source code analysis, and there are currently few studies conducted at the binary level. In this paper, we propose a method for detecting TOCTOU Race Condition in File System based on Control Flow Graph and Call Graph through Binary Analysis Platform (BAP), a binary static analysis tool.