• Journal of Information Processing Systems
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• v.15 no.3
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• pp.520-537
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• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.81-84
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• 2018

최근에 WMI를 악용한 악성코드 공격이 증가하고 있다. WMI는 악성 프로그램을 설치하지 않아도 레지스트리, 파일시스템 등 중요한 정보에 접근할 수 있다. 또한, 윈도우 운영체제에 내장된 프로그램이기 때문에 백신에서 탐지하기 어렵다. 본 논문에서는 WMI를 이용한 악성코드 탐지를 위하여 제안하는 방법은 API를 후킹하여 메모리에서 실행될 DLL을 보고 악성코드를 탐지하는 방법을 제안한다.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.78-87
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• 2021

Information technology (IT) has been applied to various fields, and currently, IT devices and systems are used in very important areas, such as aviation, industry, and national defense. Such devices and systems are subject to various types of malicious attacks, which can be software or hardware based. Compared to software-based attacks, hardware-based attacks are known to be much more difficult to detect. A hardware Trojan horse is a representative example of hardware-based attacks. A hardware Trojan horse attack inserts a circuit into an IC chip. The inserted circuit performs malicious actions, such as causing a system malfunction or leaking important information. This has increased the potential for attack in the current supply chain environment, which is jointly developed by various companies. In this paper, we discuss the future direction of research by introducing attack cases, the characteristics of hardware Trojan horses, and countermeasure trends.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.387-388
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• 2019

As malicious apps vary significantly across Android malware, it is challenging to prevent that the end-users download apps from unsecured app markets. In this paper, we propose an approach to classify the malicious methods based on permissions using Long Short Term Memory (LSTM) that is used to embed the semantics among Intent and permissions. Then the malicious method that is an unsecured method will be removed and re-uploaded to official market. This approach may induce that the end-users download apps from official market in order to reduce the risk of attacks.

• ETRI Journal
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• v.43 no.3
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.217-221
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• 2021

Recent studies have indicated that mobile markets harbor applications (apps) that are either malicious or vulnerable, compromising millions of devices. Some studies indicate that 96% of companies' employees have used at least one malicious app. Some app stores do not employ security quality attributes regarding authorization, which is the function of specifying access rights to access control resources. However, well-defined access control policies can prevent mobile apps from being malicious. The problem is that those who oversee app market sites lack the mechanisms necessary to assess mobile app security. Because thousands of apps are constantly being added to or updated on mobile app market sites, these security testing mechanisms must be automated. This paper, therefore, introduces a new mechanism for testing mobile app security, using white-box testing in a way that is compatible with Bring Your Own Device (BYOD) working environments. This framework will benefit end-users, organizations that oversee app markets, and employers who implement the BYOD trend.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1341-1351
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• 2015

As the popularization of smartphone increases the number of various applications, the number of malicious applications also grows rapidly through the third party App Market or black market. This paper suggests an investigation filter, Androfilter, that detects the fabrication of APK file effectively. Whereas the most of antivirus software uses a separate server to collect, analyze, and update malicious applications, Androfilter assumes Google Play as the trusted party and verifies integrity of an application through a simple query to Google Play. Experiment results show that Androfilter blocks brand new malicious applications that have not been reported yet as well as known malicious applications.

• Journal of Convergence Society for SMB
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• v.5 no.4
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• pp.37-42
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• 2015

Due to the advance of ICT, a variety of attacks have been developing and active. Recently, APT attacks using malicious codes have frequently occurred. Advanced Persistent Threat means that a hacker makes different security threats to attack a certain network of a company or an organization. Exploiting malicious codes or weaknesses, the hacker occupies an insider's PC of the company or the organization and accesses a server or a database through the PC to collect secrets or to destroy them. The paper suggested a countermeasure to cope with APT attacks through an APT attack process. It sought a countermeasure to delay the time to attack taken by the hacker and suggested the countermeasure able to detect and remove APT attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.431-438
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• 2019

Fileless malware uses memory injection attacks to hide traces of payloads to perform malicious works. During the memory injection attack, an attack named "process hollowing" is a method of creating paused benign process like system processes. And then injecting a malicious payload into the benign process allows malicious behavior by pretending to be a normal process. In this paper, we propose a method to detect the memory injection regardless of whether or not the malicious action is actually performed when a process hollowing attack occurs. The replication process having same execution condition as the process of suspending the memory injection is executed, the data set belonging to each process virtual memory area is compared using the fuzzy hash, and the similarity is calculated.