• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.81-87
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• 2015

Recently, with the development of the ICT environment, the use of the software is growing rapidly. And the number of the web server software used with a variety of users is also growing. However, There are also various damage cases increased due to a software security vulnerability as software usage is increasing. Especially web shell hacking which abuses software vulnerabilities accounts for a very high percentage. These web server environment damage can induce primary damage such like homepage modification for malware spreading and secondary damage such like privacy. Source code weaknesses checking system is needed during software development stage and operation stage in real-time to prevent software vulnerabilities. Also the system which can detect and determine web shell from checked code in real time is needed. Therefore, in this paper, we propose the system improving security for web server by detecting web shell attacks which are invisible to existing detection method such as Firewall, IDS/IPS, Web Firewall, Anti-Virus, etc. while satisfying existing secure coding guidelines from development stage to operation stage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.441-444
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• 2016

Peace on the Korean Peninsula is threatened by physical aggressions and cyber terrors such as nuclear tests, missile launchings, senior government officials' smart phone hackings and DDos attacks to banking systems. Cyber attacks such as vulnerability for the hackings, malware distributions are generally defended by passive defense through the detecting signs of first invasion and attack, data analysis, adding library and updating vaccine programs. In this paper the concept of security program based on Google TensorFlow machine learning ability to perform adding libraries and solving security vulnerabilities by itself is researched and proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.218-221
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• 2018

최근 악성코드의 수가 급격하게 증가하고 있으며 단순히 악성 행위를 하는 것 뿐 아니라 안티디버깅과 같은 다양한 분석 방지 기능을 탑재하여 악성코드의 분석을 어렵게 한다. 역공학 방지 기법이 적용된 지능형 악성코드를 기존 분석 도구를 사용하여 분석하면 악성행위를 하지 않거나 임의로 자기 자신을 종료시키는 방식으로 분석이 용이하지 않다. 이러한 지능형 악성코드들은 분석하기 어려울 뿐만아니라 기존 백신의 탐지 기능에 전혀 제약을 받지 않는다. 본 논문은 이와 같은 최신 지능형 악성코드에 보다 빠르게 대처하기 위해 역공학 방지 기법이 적용된 악성코드들이 메모리상에서 종료되지 않고 정상 동작하여 악성행위를 자동으로 파악할 수 있는 동적 코드 계측 프레임워크를 제안한다. 또한, 제안한 프레임워크를 개념 검증하기 위해 프로토타입을 설계 및 구현하고, 실험을 통해 그 유효성을 확인한다.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.182-186
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• 2021

The market for smart phones has been booming in the past few years. There are now over 400,000 applications on the Android market. Over 10 billion Android applications have been downloaded from the Android market. Due to the Android popularity, there are now a large number of malicious vendors targeting the platform. Many honest end users are being successfully hacked on a regular basis. In this work, a cloud based reputation security model has been proposed as a solution which greatly mitigates the malicious attacks targeting the Android market. Our security solution takes advantage of the fact that each application in the android platform is assigned a unique user id (UID). Our solution stores the reputation of Android applications in an anti-malware providers' cloud (AM Cloud). The experimental results witness that the proposed model could well identify the reputation index of a given application and hence its potential of being risky or not.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1325-1336
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• 2012

In the past about 10 different kinds of malicious code were found in one day on the average. However, the number of malicious codes that are found has rapidly increased reachingover 55,000 during the last 10 year. A large number of malicious codes, however, are not new kinds of malicious codes but most of them are new variants of the existing malicious codes as same functions are newly added into the existing malicious codes, or the existing malicious codes are modified to evade anti-virus detection. To deal with a lot of malicious codes including new malicious codes and variants of the existing malicious codes, we need to compare the malicious codes in the past and the similarity and classify the new malicious codes and the variants of the existing malicious codes. A former calculation method of the similarity on the existing malicious codes compare external factors of IPs, URLs, API, Strings, etc or source code levels. The former calculation method of the similarity takes time due to the number of malicious codes and comparable factors on the increase, and it leads to employing fuzzy hashing to reduce the amount of calculation. The existing fuzzy hashing, however, has some limitations, and it causes come problems to the former calculation of the similarity. Therefore, this research paper has suggested a new comparison method for malicious codes to improve performance of the calculation of the similarity using fuzzy hashing and also a classification method employing the new comparison method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1201-1215
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• 2015

Obfuscation tools can be used to protect android applications from reverse-engineering in android environment. However, obfuscation tools can also be misused to protect malicious applications. In order to evade detection of anti-virus, malware authors often apply obfuscation techniques to malicious applications. It is difficult to analyze the functionality of obfuscated malicious applications until it is deobfuscated. Therefore, a study on deobfuscation is certainly required to address the obfuscated malicious applications. In this paper, we analyze APKs which are obfuscated by commercial obfuscation tools and propose the deobfuscation method that can statically identify obfuscation options and deobfuscate it. Finally, we implement automatic identification and deobfuscation tool, then show the results of evaluation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.17-30
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• 2015

Unlike existing widely used bytecode-centric Android application code obfuscation methodology, our scheme in this paper makes encrypted file i.e. DEX file self-extracted arbitrary Android application. And then suggests a method regarding making the loader app to execute encrypted file's code after saving the file in arbitrary folder. Encrypted DEX file in the loader app includes original code and some of Manifest information to conceal event treatment information. Loader app's Manifest has original app's Manifest information except included information at encrypted DEX. Using our scheme, an attacker can make malicious code including obfuscated code to avoid anti-virus software at first. Secondly, Software developer can make an application with hidden main algorithm to protect copyright using suggestion technology. We implement prototype in Android 4.4.2(Kitkat) and check obfuscation capacity of malicious code at VirusTotal to show effectiveness.

• Convergence Security Journal
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• v.6 no.3
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• pp.21-28
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• 2006

Beginning flag security it was limited in Firewall but currently many information security solutions like Anti-virus, IDS, Firewall are come to be many. For efficiently managing different kinds of information security products ESM (Enterprise Security management) are developed and operated. Recently over the integrated security management system, TMS (Threat Management System) is rising in new area of interest. It follows in change of like this information security product and also collection information is being turning out diversification. For managing cyber threats, we have to analysis qualitative information (like vulnerabilities and malware codes, security news) as well as the quantity event logs which are from information security products of past. Information Threats and Vulnerability Auto Collector raises the accuracy of cyber threat judgement and can be utilized to respond the cyber threat which does not occur still by gathering qualitative information as well as quantity information.

• Convergence Security Journal
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• v.19 no.1
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• pp.11-17
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• 2019

Recently, the abuse of Internet technology has caused economic and mental harm to society as a whole. Especially, malicious code that is newly created or modified is used as a basic means of various application hacking and cyber security threats by bypassing the existing information protection system. However, research on small-capacity executable files that occupy a large portion of actual malicious code is rather limited. In this paper, we propose a model that can analyze the characteristics of known small capacity executable files by using data mining techniques and to use them for detecting unknown malicious codes. Data mining analysis techniques were performed in various ways such as Naive Bayesian, SVM, decision tree, random forest, artificial neural network, and the accuracy was compared according to the detection level of virustotal. As a result, more than 80% classification accuracy was verified for 34,646 analysis files.