• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.628-631
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• 2015

인터넷의 급속한 확산과 기술의 진보로 인해 인터넷에 대한 의존도는 갈수록 높아지고 있다. 이로 인해 웹 사이트를 기반으로 한 사이버 공격 또한 그 파급도가 점차 높아지고 있다. 특히 최근 지능화해가는 사이버 공격 과정에서 일차적 공격 수단으로 웹 사이트 기반 사이버 공격이 많이 활용되고 있다. 또한 자바 스크립트 및 HTML5의 신규 태그를 악용한 공격은 IPS나 웹 방화벽 같은 기존의 보안 장비에 탐지하기 어려운 부분이 있다. 따라서 본 논문에서는 웹 사이트를 구성하는 웹 문서에 대하여 HTML 태그 및 자바 스크립트 등에 대한 취약성을 분석하고, 분석한 결과를 토대로 위험도를 산출하는 기술을 제안하고자 한다.

• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.51-59
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• 2015

JavaScript is a popular technique for activating static HTML. JavaScript has drawn more attention following the introduction of HTML5 Standard. In proportion to JavaScript's growing importance, attacks (ex. DDos, Information leak using its function) become more dangerous. Since these attacks do not create a trail, whether the JavaScript code is malicious or not must be decided. The real attack action is completed while the browser runs the JavaScript code. For these reasons, there is a need for a real-time classification and determination technique for malicious JavaScript. This paper proposes the Analysis Engine for detecting malicious JavaScript by adopting the requirements above. The analysis engine performs static analysis using signature-based detection and dynamic analysis using behavior-based detection. Static analysis can detect malicious JavaScript code, whereas dynamic analysis can detect the action of the JavaScript code.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.1025-1036
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• 2016

The conversion of the international standard web utilization HTML5 technology is being developed for improvement of the internet environment based on nonstandard technology like ActiveX. Hyper Text Markup Language 5 (HTML5) of basic programming language for creating a web page is designed to consider the security more than HTML4. However, the range of attacks increased and a variety of security threats generated from HTML4 environment inherited by new HTML5 API. In this paper, we focus on the script-based attack such as CSRF (Cross-Site Request Forgery), Cookie Sniffing, and HTML5 API such as CORS (Cross-Origin Resource Sharing), Geolocation API related with the infringement of the personal information. We reproduced the infringement cases actually and embodied a detection module of a Plug-in type diagnosed based on client. The scanner allows it to detect and respond to the vulnerability of HTML5 previously, thereby self-diagnosing the reliability of HTML5-based web applications or web pages. In a case of a new vulnerability, it also easy to enlarge by adding another detection module.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1121-1129
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• 2013

Recent malware distribution causes severe and nation-wide problems such as 3 20 cyber attack in Korea. In particular, Drive-by download attack, which is one of attack types to distribute malware through the web, becomes the most prevalent and serious threat. To prevent Drive-by download attacks, it is necessary to analyze MDN(Malware Distribution Networks) of Drive-by download attacks. Effective analysis of MDN requires a detection of obfuscated and/or encapsulated JavaScript in a web page. In this paper, we propose the scheme called Multi-level emulation to analyze the process of malware distribution. The proposed scheme analyzes web links used for malware distribution to support the efficient analysis of MDN.