• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.305-318
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• 2023

The complexity of software products led many manufacturers to stitch open-source software for composing a product. Using open-source help reduce the development cost, but the difference in the different development life cycles makes it difficult to keep the product up-to-date. For this reason, even the patches for known vulnerabilities are not adopted quickly enough, leaving the entire product under threat. Existing studies propose to use binary differentiation techniques to determine if a product is left vulnerable against a particular vulnerability. Despite their effectiveness in finding real-world vulnerabilities, they often fail to locate the evidence of a vulnerability if it is a small function that usually is inlined at compile time. This work presents our tool FunRank which is designed to identify the short functions. Our experiments using synthesized and real-world software products show that FunRank can identify the short, inlined functions that suggest that the program is left vulnerable to a particular vulnerability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.2
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• pp.243-250
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• 2013

We have entered into an era of smart software system where the many kinds of embedded software, especially SCADA and Automotive software not only require high reliability and safety but also high-security. Removing software weakness during the software development lifecycle is very important because hackers exploit weaknesses which are source of software vulnerabilities when attacking a system. Therefore the coding rule as like core functions of MISRA-C should expand their coding focus on security. In this paper, we used CERT-C secure coding rules for nuclear-related software being developed to demonstrate high-safety software, and proposed how to remove software weakness during development.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.831-840
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• 2013

Recently, to enhance the security of software, static analysis tools for removing weaknesses, the cause of vulnerability, have been used a lot in the software development stage. Therefore, the tools need to have the rules being able to diagnose various weaknesses. Top 5 weaknesses found in the software developed by major domestic information projects from 2011 to 2012 is 76% of top 10 weaknesses per year. Software security can be improved a lot if top 5 weaknesses just are removed properly in software development. In this paper, we propose the PMD's rules for diagnosing the major weaknesses and present the results of its performance test.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2031-2036
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• 2022

Energetic particle strikes the device and induces data corruption in the configuration memory (CRAM), causing errors and even malfunctions in a system on chip (SoC). Software-based fault injection is a convenient way to assess device performance. In this paper, dynamic partial reconfiguration (DPR) is adopted to make fault injection on a Xilinx 16 nm FinFET Ultrascale+ MPSoC. And the reconfiguration module implements the Sobel and Gaussian image filtering, respectively. Fault injections are executed on the static and reconfiguration modules' bitstreams, respectively. Another contribution is that the failure modes and effects analysis (FMEA) method is applied to evaluate the system reliability, according to the obtained injection results. This paper proposes a software-based solution to estimate programmable device vulnerability.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.201-208
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• 2015

Recently computer, smart phone, medical devices, etc has become used in a variety of environments as the application fields of IT products have become diversification. Attack case of abuse of software security vulnerabilities is on the increase as the application fields of software have become diversification. Accordingly, secure coding program is of a varied but history management, updating, API module to be vulnerable to attack. Thus, this paper proposed a materialization of CMS linked system to enable check the vulnerability of the source code to content unit for secure software development, configuration management system that interwork on the transmission module. Implemented an efficient coding system secure way that departmentalized by the function of the program and by analyzing and applying secure coding standards.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.83-87
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• 2014

본 논문에서는 안드로이드 스마트폰 환경에서 높은 점유율을 가진 카카오톡 모바일 메신저 앱에 대하여 행정안전부가 고시한 Android-JAVA 시큐어 코딩가이드의 입력 데이터 검증 및 표현, API악용, 보안특성, 시간 및 상태, 에러처리, 코드 품질, 캡슐화 등 18가지 보안 취약점을 분석하고, 해당 취약점에 대한 시큐어 코딩 기법을 적용한다. 먼저 현재 상용화되고 있는 카카오톡 모바일 메신저 코드를 역공학(리버스엔지니어링)방법을 이용하여 코드단에서 소스를 분석한다. 실제 코드에서 시큐어 코딩이 안드로이드 스마트폰 환경에서 행정안전부가 고시한 Android-JAVA 시큐어 코딩가이드를 기준으로 취약한 부분을 찾고, 적용이 안 되어 있는 부분에 안드로이드 환경에 맞는 시큐어 코딩 기법을 적용한다.