• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.613-621
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• 2019

Recently, with the development of the Internet of Things (IoT) and cloud computing technologies, security threats have increased as malicious codes infect IoT devices, and new malware spreads ransomware to cloud servers. In this study, we propose a threat-detection technique that checks obfuscated script patterns to compensate for the shortcomings of conventional signature-based and behavior-based detection methods. Proposed is a malicious code-detection technique that is based on malicious script-pattern analysis that can detect zero-day attacks while maintaining the existing detection rate by registering and checking derived distribution patterns after analyzing the types of malicious scripts distributed through websites. To verify the performance of the proposed technique, a prototype system was developed to collect a total of 390 malicious websites and experiment with 10 major malicious script-distribution patterns derived from analysis. The technique showed an average detection rate of about 86% of all items, while maintaining the existing detection speed based on the detection rule and also detecting zero-day attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.785-802
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• 2017

As cyber threats using malicious code continue to increase, many security and vaccine companies are putting a lot of effort into analysis and detection of malicious codes. However, obfuscation techniques that make software analysis more difficult are applied to malicious codes, making it difficult to respond quickly to malicious codes. In particular, commercial obfuscation tools can quickly and easily generate new variants of malicious codes so that malicious code analysts can not respond to them. In order for analysts to quickly analyze the actual malicious behavior of the new variants, reverse obfuscation(=de-obfuscation) is needed to disable obfuscation. In this paper, general analysis methodology is proposed to de-obfuscate the software used by a commercial obfuscation tool, Themida. First, We describe operation principle of Themida by analyzing obfuscated executable file using Themida. Next, We extract original code and data information of executable from obfuscated executable using Pintool, DBI(Dynamic Binary Instrumentation) framework, and explain the implementation results of automated analysis tool which can deobfuscate to original executable using the extracted original code and data information. Finally, We evaluate the performance of our automated analysis tool by comparing the original executable with the de-obfuscated executable.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.605-616
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• 2020

Obfuscation technology delays the analysis of a program by modifying internal logic such as data structure and control flow while maintaining the program's functionality. However, the application of such obfuscation technology to malicious code frequently occurs to reduce the detection rate of malware in antivirus software. The obfuscation technology applied to protect software intellectual property is applied to the malicious code in reverse, which not only lowers the detection rate of the malicious code but also makes it difficult to analyze and thus makes it difficult to identify the functionality of the malicious code. The study of reverse obfuscation techniques that can be closely restored should also continue. This paper analyzes the characteristics of obfuscated code with the option of Pack the Output File and Import Protection among detailed obfuscation technologies provided by VMProtect 3.4.0, a popular tool among commercial obfuscation tools. We present a de-obfuscation algorithm.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.83-92
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• 2009

As the innovative internet technologies and multimedia are being rapidly developed, malicious codes are a remarkable new growth part and supplied by various channel. This project presents a classification methodology for malicious codes in Windows OS (Operating System) environment, develops a test classification system. Thousands of malicious codes are brought in every day. In a result, classification system is needed to analyzers for supporting information which newly brought malicious codes are a new species or a variety. This system provides the similarity for analyzers to judge how much a new species or a variety is different to the known malicious code. It provides to save time and effort, to less a faulty analysis. This research includes the design of classification system and test system. We classify the malicious codes to 9 groups and then 9 groups divide the clusters according to the each property.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.21-31
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• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.291-297
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• 2021

Recently, as the number of new and variant malicious codes has increased exponentially, malware warnings are being issued to PC and smartphone users. Malware is becoming more and more intelligent. Efforts to protect personal information are becoming more and more important as social issues are used to stimulate the interest of PC users and allow users to directly download malicious codes. In this way, it is difficult to prevent malicious code because malicious code infiltrates in various forms. As a countermeasure to solve these problems, many studies are being conducted to apply deep learning. In this paper, we investigate and analyze various deep learning methods to detect and classify malware.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.593-603
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• 2020

The advent of malicious code has increased exponentially due to the spread of malicious code generation tools in accordance with the development of the network, but there is a limit to the response through existing malicious code detection methods. According to this situation, a machine learning-based malicious code detection method is evolving, and in this paper, the feature of data is extracted from the PE header for machine-learning-based malicious code detection, and then it is used to automate the malware through autoencoder. Research on how to extract the indicated features and feature importance. In this paper, 549 features composed of information such as DLL/API that can be identified from PE files that are commonly used in malware analysis are extracted, and autoencoder is used through the extracted features to improve the performance of malware detection in machine learning. It was proved to be successful in providing excellent accuracy and reducing the processing time by 2 times by effectively extracting the features of the data by compressively storing the data. The test results have been shown to be useful for classifying malware groups, and in the future, a classifier such as SVM will be introduced to continue research for more accurate malware detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.61-75
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• 2011

Recently, there is significant increasing of massive attacks, which try to infect PCs that visit websites containing pre-implanted malicious code. When visiting the websites, these hidden malicious codes can gain monetary profit or can send various cyber attacks such as BOTNET for DDoS attacks, personal information theft and, etc. Also, this kind of malicious activities is continuously increasing, and their evasion techniques become professional and intellectual. So far, the current signature-based detection to detect websites, which contain malicious codes has a limitation to prevent internet users from being exposed to malicious codes. Since, it is impossible to detect with only blacklist when an attacker changes the string in the malicious codes proactively. In this paper, we propose a novel approach that can detect unknown malicious code, which is not well detected by a signature-based detection. Our method can detect new malicious codes even though the codes' signatures are not in the pattern database of Anti-Virus program. Moreover, our method can overcome various obfuscation techniques such as the frequent change of the included redirection URL in the malicious codes. Finally, we confirm that our proposed system shows better detection performance rather than MC-Finder, which adopts pattern matching, Google's crawling based malware site detection, and McAfee.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.429-442
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• 2020

The proportion of attacks via office documents is increasing in recent incidents. Although the security of office applications has been strengthened gradually, the attacks through the office documents are still effective due to the sophisticated use of social engineering techniques and advanced attack techniques. In this paper, we propose a method for detecting malicious OOXML(Office Open XML) documents and a framework for detection. To do this, malicious files used in the attack and benign files were collected from the malicious code repository and the search engine. By analyzing the malicious code types of collected files, we identified six "suspicious object" elements that are meaningful in determining whether they are malicious in a document. In addition, we implemented an OOXML document-based malware detection framework based on the detection method to classify the collected files and found that 98.45% of malicious filesets were detected.