• Journal of Information Processing Systems
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• v.16 no.4
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.85-94
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• 2014

This paper studied the detection technique using file DNA-based behavior pattern analysis in order to minimize damage to user system by malicious programs before signature or security patch is released. The file DNA-based detection technique was applied to defend against zero day attack and to minimize false detection, by remedying weaknesses of the conventional network-based packet detection technique and process-based detection technique. For the file DNA-based detection technique, abnormal behaviors of malware were splitted into network-related behaviors and process-related behaviors. This technique was employed to check and block crucial behaviors of process and network behaviors operating in user system, according to the fixed conditions, to analyze the similarity of behavior patterns of malware, based on the file DNA which process behaviors and network behaviors are mixed, and to deal with it rapidly through hazard warning and cut-off.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.533-536
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• 2012

인터넷의 급격한 성장과 함께 컴퓨터 통신 이용률이 폭발적으로 증가함에 따라 여러 악성코드가 등장하게 되었다. 이러한 악성코드는 시스템의 비정상 동작 유발, 네트워크 성능 저하, 개인정보유출의 문제를 발생시킨다. 현재의 악성코드 분석은 Signature 분석이 대부분이며, Signature 분석은 특정 패턴의 악성코드는 빠르게 탐지하나, 변조된 코드는 탐지하지 못하며, 이미 피해가 널리 퍼진 뒤 분석 및 차단이 가능하다는 단점을 가진다. 따라서 본 논문은 NDIS(Network Driver Interface Specification)를 이용하여 악성코드에 대해 수동적인 Signature 분석의 단점을 보완 하는 시스템 및 네트워크 상태 분석모델을 제시 하여 보다 능동적인 탐지 및 차단 프로세스를 정의하고, 모델 구현을 위한 방법을 제시한다.