• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.963-974
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• 2018

With the growing interest in cryptocurrency such as bitcoin, the blockchain technology has attracted much attention in various applications as a distributed security platform with excellent security. However, Cryptojacking, an attack that hijack other computer resources such as CPUs, has occured due to vulnerability to the Cryptomining process. In particular, browser-based Cryptojacking is considered serious because attacks can occur only by visiting a Web site without installing it on a visitor's PC. The current Cryptojacking detection system is mostly signature-based. Signature-based detection methods have problems in that they can not detect a new Cryptomining code or a modification of existing Cryptomining code. In this paper, we propose a Cryptojacking detection solution using a dynamic analysis-based that uses a headless browser to detect unknown Cryptojacking attacks. The proposed dynamic analysis-based Cryptojacking detection system can detect new Cryptojacking site that cannot be detected in existing signature-based Cryptojacking detection system and can detect it even if it is called or obfuscated by bypassing Cryptomining code.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.775-784
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• 2019

Malware infringement attacks are continuously increasing in various environments such as mobile, IOT, windows and mac due to the emergence of new and variant malware, and signature-based countermeasures have limitations in detection of malware. In addition, analytical performance is deteriorating due to obfuscation, packing, and anti-VM technique. In this paper, we propose a system that can detect malware based on machine learning by using similarity hashing-based pattern detection technique and static analysis after file classification according to packing. This enables more efficient detection because it utilizes both pattern-based detection, which is well-known malware detection, and machine learning-based detection technology, which is advantageous for detecting new and variant malware. The results of this study were obtained by detecting accuracy of 95.79% or more for benign sample files and malware sample files provided by the AI-based malware detection track of the Information Security R&D Data Challenge 2018 competition. In the future, it is expected that it will be possible to build a system that improves detection performance by applying a feature vector and a detection method to the characteristics of a packed file.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.

• 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 Internet Computing and Services
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• v.20 no.2
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• pp.61-68
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• 2019

Although the number of smartphone users is continuously increasing due to the advantage of being able to easily use most of the Internet services, the number of counterfeit applications is rapidly increasing and personal information stored in the smartphone is leaked to the outside. Because Android app was developed with Java language, it is relatively easy to create counterfeit apps if attacker performs the de-compilation process to reverse app by abusing the repackaging vulnerability. Although an obfuscation technique can be applied to prevent this, but most mobile apps are not adopted. Therefore, it is fundamentally impossible to block repackaging attacks on Android mobile apps. In addition, personal information stored in the smartphone is leaked outside because it does not provide a forgery self-verification procedure on installing an app in smartphone. In order to solve this problem, blockchain is used to implement a process of certificated application registration and a fake app identification and detection mechanism is proposed on Hyperledger Fabric framework.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.667-677
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• 2012

Vulnerable web applications have been the primary method used by the attackers to spread their malware to a large number of victims. Such attacks commonly make use of malicious links to remotely execute a rather advanced malicious code. The attackers often deploy malwares that utilizes unknown vulnerabilities so-called "zero-day vulnerabilities." The existing computer vaccines are mostly signature-based and thus are effective only against known attack patterns, but not capable of detecting zero-days attacks. To mitigate such limitations of the current solutions, there have been a numerous works that takes a behavior-based approach to improve detection against unknown malwares. However, behavior-based solutions arbitrarily introduced a several limitations that made them unsuitable for real-life situations. This paper proposes an advanced web browser based malicious behavior detection system that solves the problems and limitations of the previous approaches.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.95-107
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• 2022

With the rapid development of Internet of Things sensor devices and big data processing techniques, Internet of Things sensor-based information systems have been applied in various industries. Depending on the industry in which the information systems are applied, the accuracy of the information derived can affect the industry's efficiency and safety. Therefore, security techniques that protect sensing data from security attacks and enable information systems to derive accurate information are essential. In this paper, we examine security threats targeting each processing step of an Internet of Things sensor-based information system and propose security mechanisms for each security threat. Furthermore, we present an Internet of Things sensor-based information system structure that is robust to security attacks by integrating the proposed security mechanisms. In the proposed system, by applying lightweight security techniques such as a lightweight encryption algorithm and obfuscation-based data validation, security can be secured with minimal processing delay even in low-power and low-performance IoT sensor devices. Finally, we demonstrate the feasibility of the proposed system by implementing and performance evaluating each security mechanism.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.23-40
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• 2024

Hardware attacks involve physical reverse engineering efforts to steal sensitive information, such as encryption keys and circuit designs. Encryption and obfuscation are representative countermeasures, but they are nullified if adversaries manage to find the key. To address this issue, we propose e-Cryptex, which utilizes a Physically Unclonable Function (PUF) as an anti-tampering shield. PUF acts as a random number generator and relies on unique physical variants that cannot be replicated or restored to enhance anti-tampering mechanisms. e-Cryptex uses PUF as a shield to protect the system's structure and generate the key. Tampering with the shield will result in the destruction of the key. This paper demonstrates that e-Cryptex meets PUF security requirements and is effective in detecting of tampering attempts that pierce or completely destroy the shield. Each board consistently generates the same key under normal conditions, while also showing key uniqueness across different boards.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.67-75
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• 2018

According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.