• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.327-328
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• 2024

본 논문에서는 DarkWebGuard라는 실시간 악성 URL 탐지 시스템을 소개하고, 그 개발에 사용된 도구와 알고리즘에 대해 논의합니다. DarkWebGuard는 머신러닝을 기반으로 하며, 인터넷 보안에 대한 현재의 요구를 충족시키기 위해 개발되었습니다. 이 시스템은 사용자와 시스템을 보호하기 위해 악성 URL을 실시간으로 탐지하고 분류합니다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.365-367
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• 2014

최근 웹 서비스의 증가와 악성코드는 그 수를 판단 할 수 없을 정도로 빠르게 늘어나고 있다. 매년 늘어나는 악성코드는 금전적 이윤 추구가 악성코드의 주된 동기가 되고 있으며 이는 공공기관 및 보안 업체에서도 악성코드를 탐지하기 위한 연구가 활발히 진행되고 있다. 본 논문에서는 실시간으로 패킷을 분석할수 있는 필터링과 웹 크롤링을 통해 도메인 및 하위 URL까지 자동적으로 탐지할 수 있는 악성코드 탐지 시스템을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1786-1793
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• 2022

Cyber-attacks such as smishing and hacking mail exploiting COVID-19, political and social issues, have recently been continuous. Machine learning and deep learning technology research are conducted to prevent any damage due to cyber-attacks inducing malicious links to breach personal data. It has been concluded as a lack of basis to judge the attacks to be malicious in previous studies since the features of data set were excessively simple. In this paper, nine main features of three types, "URL Days", "URL Word", and "URL Abnormal", were proposed in addition to lexical features of URL which have been reflected in previous research. F1-Score and accuracy index were measured through four different types of machine learning algorithms. An improvement of 0.9% in a result and the highest value, 98.5%, were examined in F1-Score and accuracy through comparatively analyzing an existing research. These outcomes proved the main features contribute to elevating the values in both accuracy and performance.

• 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 the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.869-882
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• 2012

With the growth of Web services and the development of web exploit toolkits, web-based malware has increased dramatically. Using Javascript Obfuscation, recent web-based malware hide a malicious URL and the exploit code. Thus, pattern matching for network intrusion detection systems has difficulty of detecting malware. Though various methods have proposed to detect Javascript malware on a users' web browser, the overall detection is needed to counter advanced attacks such as APTs(Advanced Persistent Treats), aimed at penetration into a certain an organization's intranet. To overcome the limitation of previous pattern matching for network intrusion detection systems, a novel deobfuscating method to handle obfuscated Javascript is needed. In this paper, we propose a framework for effective hidden malware detection through an automated deobfuscation regardless of advanced obfuscation techniques with overriding JavaScript functions and a separate JavaScript interpreter through to improve jsunpack-n.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1202-1205
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• 2010

무작위 SQL 삽입 공격 등을 통해 웹서버 해킹 사례가 꾸준히 증가하고 있으며, 대부분의 해킹된 웹서버는 난독화된 자바 스크립트 코드가 웹페이지에 삽입되어 악성코드 경유/유포지로 악용되고 있다. 본 논문에서는 난독화된 자바 스크립트 복원 및 취약한 ActiveX 생성에 사용되는 주요 함수에 대해 후킹 기술을 적용한 브라우저를 이용해서 난독화된 스크립트를 자동으로 해독하고, 악성코드 경유/유포지로 악용되는 웹 서버를 탐지할 수 있는 기술을 제안한다. 또한 제안 기술을 프로토타입 시스템으로 구현하고, 악성 URL 공유 사이트를 통해 수집한 난독화된 자바 스크립트 샘플 분석을 통해 제안한 기술이 높은 악성코드 경유/유포지 탐지율을 보이는 것을 증명한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1227-1233
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• 2021

QR Code has been used in various forms such as simple business cards and URLs. Recently, the influence of Corona 19 Fundemik has led to the use of QR Codes to track travel routes through visits and entry / exit records, and QR Code usage has skyrocketed. In this way, most people have come to use it in the masses and are constantly under threat. In the case of QR Code, you do not know what you are doing until you execute it. Therefore, if you undoubtedly execute a QR Code with a malicious URL inserted, you will be directly exposed to security threats. Therefore, this paper provides a cloud-based malware QR Code detection system that can make a normal connection only when there is no abnormality after determining whether it is a malicious QR Code when scanning the QR Code.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1185-1195
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• 2014

Domestic and international CERTs are carrying out security monitoring and response services based on security devices for intrusion incident prevention and damage minimization of the organizations. However, the security monitoring and response service has a fatal limitation in that it is unable to detect unknown attacks that are not matched to the predefined signatures. In recent, many approaches have adopted the darknet technique in order to overcome the limitation. Since the darknet means a set of unused IP addresses, no real systems connected to the darknet. Thus, all the incoming traffic to the darknet can be regarded as attack activities. In this paper, we present a collection and analysis method of malicious URLs based on darkent traffic for advanced security monitoring and response service. The proposed method prepared 8,192 darknet space and extracted all of URLs from the darknet traffic, and carried out in-depth analysis for the extracted URLs. The analysis results can contribute to the emergence response of large-scale cyber threats and it is able to improve the performance of the security monitoring and response if we apply the malicious URLs into the security devices, DNS sinkhole service, etc.

• Journal of IKEEE
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• v.25 no.4
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• pp.672-678
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• 2021

New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.