• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1365-1373
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• 2019

Meltdown and Spectre are vulnerabilities that exploit out-of-order execution and speculative execution techniques to read memory regions that are not accessible with user privileges. OS patches were released to prevent this attack, but older systems without appropriate patches are still vulnerable. Currently, there are some research to detect Meltdown and Spectre attacks, but most of them proposed dynamic analysis methods. Therefore, this paper proposes a binary signature that can be used to detect Meltdown and Spectre malware without executing them. For this, we collected 13 malicious codes from GitHub and performed binary pattern analysis. Based on this, we proposed a static detection method for Meltdown and Spectre malware. Our results showed that the method identified all the 19 attack files with 0.94% false positive rate when applied to 2,317 normal files.

• 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 IKEEE
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• v.26 no.3
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• pp.380-388
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• 2022

The traditional malware detection technologies collect known malicious programs and analyze their characteristics. Then such a detection technology makes a blacklist based on the analyzed malicious characteristics and checks programs in the user's system based on the blacklist to determine whether each program is malware. However, such an approach can detect known malicious programs, but responding to unknown or variant malware is challenging. In addition, since such detection technologies generally monitor all programs in the system in real-time, there is a disadvantage that they can degrade the system performance. In order to solve such problems, various methods have been proposed to analyze major behaviors of malicious programs and to respond to them. The main characteristic of ransomware is to access and encrypt the user's file. So, a new approach is to produce the whitelist of programs installed in the user's system and allow the only programs listed on the whitelist to access the user's files. However, although it applies such an approach, attackers can still perform malicious behavior by performing a DLL(Dynamic-Link Library) injection attack on a regular program registered on the whitelist. This paper proposes a method to respond effectively to attacks using DLL injection.

• Journal of Convergence Society for SMB
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• v.5 no.1
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• pp.19-23
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• 2015

Distributed Denial of Service attack is a form of denial of service attacks, the attacker to attack a place in a number of points of attack by a wide variety of forms over the network to perform a service on a point attack . Do not use a specific server or client attempts to make a connection to many services available that prevents this attack and so normally used . Corresponding methods of DDoS attacks has a corresponding managerial aspects and technical aspects of the proposed two.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.617-635
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• 2021

Internet of Things (IoT) devices connected to the network without appropriate security solutions have become a serious security threat to ICT infrastructure. Moreover, due to the nature of IoT devices, it is difficult to apply currently existing security solutions. As a result, IoT devices have easily become targets for cyber attackers, and malware attacks on IoT devices are actually increasing every year. Even though several security solutions are being developed to protect IoT infrastructure, there is a great risk to apply unverified security solutions to real-world environments. Therefore, verification tools to verify the functionality and performance of the developed security solutions are also needed. Furthermore, just as security threats vary, there are several security solution s that defend against them, requiring suitable verification tools based on the characteristics of each security solution. In this paper, we propose an high-speed malware propagation tool that spreads malware at high speed in the IoT infrastructure. Also, we can verify the functionality of the security solution that detect and quickly block attacks spreading in IoT infrastructure by using the high-speed malware propagation tool.

• 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 the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1067-1076
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• 2023

Recently, as variant malware has increased, the scale of cyber hacking incidents is expanding. To respond to intelligent cyberhacking attack, machine learning-based research is actively underway to effectively classify malware families. However, existing classification models have problems where performance deteriorates when the dataset is obfuscated or sparse. In this paper, we propose a hybrid dataset that combines features extracted from ASM files and BYTES files, and evaluate classification performance using FNN. As a result of the experiment, the proposed method showed performance improvement of about 4% compared to a single dataset, and in particular, performance improvement of about 30% for rare families.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.377-386
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• 2016

As the number of malware has been increased, it is necessary to analyze malware rapidly against cyber attack. Additionally, Dynamic malware analysis has been widely studied to overcome the limitation of static analysis such as packing and obfuscation, but still has a problem of exploring multiple execution path. Previous works for exploring multiple execution path have several problems that it requires much time to analyze and resource for preparing analysis environment. In this paper, we proposed efficient exploring approach for multiple execution path in a single analysis environment by pipelining processes and showed the improvement of speed by 29% in 2-core and 70% in 4-core through experiment.

• Convergence Security Journal
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• v.8 no.2
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• pp.63-70
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• 2008

In order not to make the malwares be easily analyzed, the hackers apply various anti-reversing and obfuscation techniques to the malwares. However, as the more anti-revering techniques are applied to the malwares the more abnormal characteristics in the PE file's header which are not shown in the normal PE file, could be observed. In this letter, a new malware detection technique is proposed based on this observation. For the malware detection, we define the Characteristics Vector(CV) which can represent the characteristics of a PE file's header. In the learning phase, we calculate the average CV(ACV) of malwares(ACVM) and normal files(ACVN). To detect the malwares we calculate the 2 Weighted Euclidean Distances(WEDs) from a file's CV to ACVs and they are used to decide whether the file is a malware or not. The proposed technique is very fast and detection rate is fairly high, so it could be applied to the network based attack detection and prevention devices. Moreover, this technique is could be used to detect the unknown malwares because it does not utilize a signature but the malware's characteristics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.459-469
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• 2023

The number of IoT devices is explosively increasing due to the development of embedded equipment and computer networks. As a result, cyber threats to IoT are increasing, and currently, malicious codes are being distributed and infected to IoT devices and exploited for DDoS. Currently, IoT devices that are the target of such an attack have various installation environments and have limited resources. In addition, IoT devices have a characteristic that once set up, the owner does not care about management. Because of this, IoT devices are becoming a blind spot for management that is easily infected with malicious codes. Because of these difficulties, the threat of malicious codes always exists in IoT devices, and when they are infected, responses are not properly made. In this paper, we will design an malware detection system for IoT in consideration of the characteristics of the IoT environment and present detection rules suitable for use in the system. Using this system, it will be possible to construct an IoT malware detection system inexpensively and efficiently without changing the structure of IoT devices that are already installed and exposed to cyber threats.