• Convergence Security Journal
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• v.15 no.1
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• pp.3-9
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• 2015

In this paper, a detection technique of smishing using a TaintDroid is suggested. Suggesting system detects malicious acts by transmitting a URL to the TaintDroid server and installing a relevant application to a virtual device of the TaintDroid server, when a smartphone user receives a text message including the URL suspected as a smishing. Through this we want to distinguish an application that can not install because of suspicion of a smishing in an actual smartphone whether said application is malicious application or not by testing with the virtual device of said system. The detection technique of a smishing using the TaintDroid suggested in this paper is possible to detect in a new form a smishing with a text message and to identifying which application it is through analysis of results from a user.

• Journal of Animal Science and Technology
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• v.66 no.2
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• pp.387-397
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• 2024

Pork quality is determined by several attributes, among which odor and taste are the utmost significant. Therefore, this study was aimed to assess the effects of boar odor hormone concentration on the quality traits and sensory acceptability of pork. A total twenty-six (26) non-castrated 3-way crossbred (Landrace × Yorkshire × Duroc) pigs were selected with an average body weight (ABW) 115.6 kg before to slaughter. The three treatment groups (low, medium and high) were divided according to the androstenone concentration. In experiment 1, for meat quality traits carcass was selected based on androstenone concentration: low (LC, 0.64-0.69 ㎍/g, n = 9), medium (MC, 0.70-0.99 ㎍/g, n = 7) and high (HC, 1.00-1.69 ㎍/g, n = 10). In experiment 2, for sensory evaluation carcasses were also selected based on the abovementioned conditions. Results revealed that androstenone concentration not effect on proximate components, meat quality traits and fatty acids except palmitoleic acid. Sensory evaluation data showed that boar taint and meat boar taint were significantly increased in a concentration-dependent manner from low to high, whereas, gravy and meat flavor preference were significantly increased in LC group than HC group. In addition, correlation analysis showed that boar taint and meat boar taint were positively, and gravy and meat flavor preference were negatively correlated with boar taint hormones. In essence, our findings indicate that androstenone concentration had no effect on meat qualities, but a high concentration of androstenone had a negative effect on the sensory characteristics in uncastrated pigs.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.7
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• pp.303-310
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• 2013

As the role of software increases in computing environments, issues in software security become more important problems. Dynamic taint analysis is a technique to trace and manage tainted data originated from unreliable sources during the execution of a program. This analysis can be applied to software security verification as well as software behavior understanding, testing unexpected errors, or debugging. In the previous researches, they focussed only to show the analysis results of dynamic taint analysis, and they did not logically describe propagation process of tainted data and analysis procedures. So, there were difficulties in understanding the analysis procedures or applying to other analysis. In this paper, by theoretically describing the analysis procedure, we logically show how the propagation process of tainted data can be traced, and present a theoretical model for dynamic taint analysis. In addition, we verify the correctness of the proposed model by implementing an analyser, and show that propagation of tainted data can be traced by the model. The proposed model can be applied to understand the analysis procedures of data flows in dynamic taint analysis, and can be used as an base knowledge for designing and implementing analysis method, which applies such analysis method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2576-2590
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• 2020

The fundamental reason why most SQL injection detection methods are difficult to use in practice is the low reusability of the implementation code. This paper presents a reusable SQL injection detection method for Java Web applications based on AOP (Aspect-Oriented Programming) and dynamic taint analysis, which encapsulates the dynamic taint analysis processes into different aspects and establishes aspect library to realize the large-grained reuse of the code for detecting SQL injection attacks. A metamodel of aspect library is proposed, and a management tool for the aspect library is implemented. Experiments show that this method can effectively detect 7 known types of SQL injection attack such as tautologies, logically incorrect queries, union query, piggy-backed queries, stored procedures, inference query, alternate encodings and so on, and support the large-grained reuse of the code for detecting SQL injection attacks.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.109-110
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• 2011

악성코드는 루트킷, Anti-VM/디버깅, 실행압축 등 기술사용으로 점차 지능화된 형태로 발전하고 있다. 이에 대응하기 위해, user 및 kernel level에서의 다양한 행위 기반 분석기술이 연구되고 있으나, 이를 회피하는 악성코드가 지속적으로 출현하고 있다. 본 논문에서는 Taint Analysis 기반 악성코드 탐지방안을 제시한다. 본 대응기술은 공격자에 의해 회피하기 어렵고, 의심스러운 데이터 유형별 선별적 분석이 가능하여 행위 기반 대응기술의 한계를 보완할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.2
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• pp.263-277
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• 2021

Taint analysis techniques are popularly used to detect web vulnerabilities originating from unverified user input data, such as Cross-Site Scripting (XSS) and SQL Injection, in web applications written in JavaScript. To detect such vulnerabilities, it would be necessary to trace variables affected by user-submitted inputs. However, because of the dynamic nature of JavaScript, it has been a challenging issue to identify those variables without running the web application code. Therefore, most existing taint analysis tools have been developed based on dynamic taint analysis, which requires the overhead of running the target application. In this paper, we propose a novel static taint analysis technique using symbol information obtained from the TypeScript (a superset of JavaScript) compiler to accurately track data flow and detect security vulnerabilities in TypeScript code. Our proposed technique allows developers to annotate variables that can contain unverified user input data, and uses the annotation information to trace variables and data affected by user input data. Since our proposed technique can seamlessly be incorporated into the TypeScript compiler, developers can find vulnerabilities during the development process, unlike existing analysis tools performed as a separate tool. To show the feasibility of the proposed method, we implemented a prototype and evaluated its performance with 8 web applications with known security vulnerabilities. We found that our prototype implementation could detect all known security vulnerabilities correctly.

• ETRI Journal
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• v.38 no.2
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• pp.376-386
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• 2016

Currently, web applications are gaining in prevalence. In a web application, an input may not be appropriately validated, making the web application susceptible to cross-site scripting (XSS), which poses serious security problems for Internet users and websites to whom such trusted web pages belong. A taint inference is a type of information flow analysis technique that is useful in detecting XSS on the client side. However, in existing techniques, two current practical issues have yet to be handled properly. One is URL rewriting, which transforms a standard URL into a clearer and more manageable form. Another is HTML sanitization, which filters an input against blacklists or whitelists of HTML tags or attributes. In this paper, we make an analogy between the taint inference problem and the molecule sequence alignment problem in bioinformatics, and transfer two techniques related to the latter over to the former to solve the aforementioned yet-to-be-handled-properly practical issues. In particular, in our method, URL rewriting is addressed using local sequence alignment and HTML sanitization is modeled by introducing a removal gap penalty. Empirical results demonstrate the effectiveness and efficiency of our method.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.257-262
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• 2015

As software grows more complex, it contains more bugs that are not recognized by developers. Attackers can then use exploitable bugs to penetrate systems or spread malicious code. As a representative method, attackers manipulated documents or multimedia files in order to make the software engage in unanticipated behavior. Recently, this method has gained frequent use in A.P.T. In this paper, an automatic analysis method to find software security bugs was proposed. This approach aimed at finding security bugs in the software which can arise from input data such as documents or multimedia. Through dynamic taint analysis, how input data propagation to vulnerable code occurred was tracked, and relevant instructions in relation to input data were found. Next, the relevant instructions were translated to a formula and vulnerable input data were found via the formula using an SMT solver. Using this approach, 6 vulnerable codes were found, and data were input to crash applications such as HWP and Gomplayer.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.417-437
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• 2022

The quality of the fuzzing seed file is one of the important factors to discover vulnerabilities faster. Although the prior seed generation paradigm, using dynamic taint analysis and symbolic execution techniques, enhanced fuzzing efficiency, the yare not extensively applied owing to their high complexity and need for expertise. This study proposed the DDRFuzz system, which creates seed files based on sequence-to-sequence models. We evaluated DDRFuzz on five open-source applications that used multimedia input files. Following experimental results, DDRFuzz showed the best performance compared with the state-of-the-art studies in terms of fuzzing efficiency.