• Smart Media Journal
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• v.8 no.1
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• pp.19-26
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• 2019

At the opening ceremony of 2018 Winter Olympics in PyeongChang, an unknown cyber-attack occurred. The malicious code used in the attack is based on in-memory malware, which differs from other malicious code in its concealed location and is spreading rapidly to be found in more than 140 banks, telecommunications and government agencies. In-memory malware accounts for more than 15% of all malicious codes, and it does not store its own information in a non-volatile storage device such as a disk but resides in a RAM, a volatile storage device and penetrates into well-known processes (explorer.exe, iexplore.exe, javaw.exe). Such characteristics make it difficult to analyze it. The most recently released in-memory malicious code bypasses the endpoint protection and detection tools and hides from the user recognition. In this paper, we propose a method to efficiently extract the payload by unpacking injection through IDA Pro debugger for Dorkbot and Erger, which are in-memory malicious codes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.754-762
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• 2010

Nowadays, email-based targeted attacks using malcode-bearing documents have been steadily increased. To improve the success rate of the attack and avoid anti-viruses, attackers mainly employ zero-day exploits and relevant social engineering techniques. In this paper, we propose an architecture of the email vaccine cloud system to prevent targeted attacks using malcode-bearing documents. The system extracts attached document files from email messages, performs behavior analysis as well as signature-based detection in the virtual machine environment, and completely removes malicious documents from the messages. In the process of behavior analysis, the documents are regarded as malicious ones in cases of creating executable files, launching new processes, accessing critical registry entries, connecting to the Internet. The email vaccine cloud system will help prevent various cyber terrors such as information leakages by preventing email based targeted attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1327-1337
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• 2019

With the growth of the IoT market, malware security threats are steadily increasing for devices that use the linux architecture. However, except for the major malware causing serious security damage such as Mirai, there is no related technology or research of security community about linux malware. In addition, the diversity of devices, vendors, and architectures in the IoT environment is further intensifying, and the difficulty in handling linux malware is also increasing. Therefore, in this paper, we propose an analysis system based on ELF which is the main format of linux architecture, and a binary based analysis system considering IoT environment. The ELF-based analysis system can be pre-classified for a large number of malicious codes at a relatively high speed and a relatively low-speed binary-based analysis system can classify all the data that are not preprocessed. These two processes are supposed to complement each other and effectively classify linux-based malware.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.283-293
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• 2012

A new type of malware that extracts personal and account data over an extended period of time and that apparently is resistant to detection by vaccines has been identified. Generally, a malware is installed on a computer through network-to-network connections by utilizing Web vulnerabilities that contain injection, XSS, broken authentication and session management, or insecure direct-object references, among others. After the malware executes registration of an arbitrary service and an arbitrary process on a computer, it then periodically communicates the collected confidential information to a hacker. This paper is a systematic approach to analyzing a new type of malware called "winweng," a kind of worm that frequently made appearances during the first half of 2011. The research describes how the malware came to be in circulation, how it infects computers, how its operations expose its existence and suggests improvements in responses and countermeasures. Keywords: Malware, Worm, Winweng, SNORT.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.55-63
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• 2021

Recently, the incidence rate of malicious codes is over tens of thousands of cases, and it is known that it is almost impossible to detect/respond all of them. This study proposes a method for detecting multiple behavior patterns based on a graph database as a new method for dealing with malicious codes. Traditional dynamic analysis techniques and has applied a method to design and analyze graphs of representative associations malware pattern(process, PE, registry, etc.), another new graph model. As a result of the pattern verification, it was confirmed that the behavior of the basic malicious pattern was detected and the variant attack behavior(at least 5 steps), which was difficult to analyze in the past. In addition, as a result of the performance analysis, it was confirmed that the performance was improved by about 9.84 times or more compared to the relational database for complex patterns of 5 or more steps.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.800-803
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• 2011

악성코드에 감염된 여러 대의 좀비 PC가 특정 사이트를 공격하는 해킹 방식인 DDoS 공격은 최근 7.7 DDoS 대란을 비롯하여, 1년도 채 되지 않아 3.3 DDoS 대란으로 이어지고 있다. DDoS 대란의 발생을 통해 사이버 보안 위협의 위험성이 점차 증가하고 있음을 확인할 수 있으며, 공격 경로를 통해 사용자 PC로 유입된 악성코드는 사용자의 자산인 PC에 저장되어 있는 정보들을 모두 삭제할 수 있어 공격으로 인해 발생하는 금전적, 정신적 피해가 점차 심각해지고 있다. 이러한 환경에서 조직 및 사용자가 보존해야 할 정보 자산의 기밀성, 무결성, 가용성을 실현하기 위하여 정보보호관리체계를 기반으로 지속적인 점검을 수행하여 조직 내의 위기관리 프로세스가 구축되어야 한다. 따라서 본 논문에서는 기업이 보유하고 있는 정보 자산이 외부로 유출되는 것을 방지하고, 악의적인 악성코드가 내부로 유입 되어 조직 내부의 자산을 파괴하는 위협으로부터 보안을 제공하기 위한 정보보호관리체계에 대해서 분석하고, 기업 정보보안 기능을 강화할 수 있는 방안에 대하여 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.395-397
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• 2014

국내에서 가장 폭넓게 사용되는 모바일 운영체제인 안드로이드는 수 많은 악성코드에 대한 위협 속에 있다. 그 중에서 가장 위협적인 공격은 루트 권한을 획득하는 악성코드이다. 따라서 본 연구는 가상화 환경을 통해 안드로이드 시스템에서 실존하는 루트 권한 획득을 탐지하는 시스템을 소개 하고 있다. 이를 위해 CPU 제조사에서 제공하는 가상화 기반 기술을 활용하였으며 결과적으로 시스템 상에서 루트 권한으로 동작하는 프로세스를 감지할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.781-784
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• 2012

스마트폰의 사용이 늘어나면서 스마트폰의 취약점을 노리는 악성코드들도 많이 발생되고 있다. 특히 악성코드를 숨겨주는 루트킷이 최근 캐리어IQ사태를 통해 이슈가 되면서 루트킷에 대한 관심이 늘어가고 있다. 루트킷은 동작방식에 따라 유저모드 루트킷과 커널모드 루트킷으로 나눌 수 있는데 PC처럼 운영체제를 통해 자원 및 프로세스를 제어하는 스마트폰도 루트킷의 위협에 안전하지 못하다. 본 논문은 PC환경에서 동작하는 루트킷의 동작원리를 파악하고 스마트폰 환경 특히 안드로이드 환경의 유저모드 루트킷의 동작과 위협을 주지시키고자 한다.