• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.304-313
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• 2010

There are several methods for malware analyses. However, it is difficult to detect malware exactly with existing detection methods. Especially, malware with strong anti-debugging facilities can detect analyzer and disturb their analyses. Furthermore, it takes too much time to analyze malware. In order to resolve these problems of current analyzers, more improved analysis scheme is required. This paper suggests a dynamic binary instrumentation which supports the instruction analysis and the memory access tracing. Additionally, by supporting the API call tracing with the DLL loading analysis, our system establishes the foundation for analyzing various executable codes. Based on Xen, full-virtualization environment is built using Intel's VT technology. Windows XP can be used as a guest. We analyze representative malware using several functions of our system, and show the accuracy and efficiency enhancements in binary analyses capability of our system.

• 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.25 no.6
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• pp.1385-1397
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• 2015

The Proxy DLL is a mechanism using a normal characteristics of Windows. Specific malware is executed via this mechanism after intrusion into a system which is targeted. If a intrusion of malware is successful, malware should be executed at least once. For execution, malware is disguised as a Windows Library. The malware of Winnti group is a good case for this. Winnti is a group of Chinese hacking groups identified by research in the fall of 2011 at Kaspersky Lab. Winnti group activities was negatively over the years to target the online video game industry, in this process by making a number of malware infected the online gaming company. In this paper, we perform research on detection techniques of Proxy DLL malware which is disguised as a Windows library through Winnti group case. The experiments that are undertaken to target real malware of Winnti show reliability of detection techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.117-124
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• 2016

Virtual environment is widely used for analyzing malware which is increasing very rapidly. However, knowing this trend, hackers are adopting virtual environment detection techniques for malware to kill itself or stop malicious behaviors when detecting virtual environments. Various research is going on in order to thwart any efforts to utilize anti-virtualization techniques, but until now several techniques can evade most of well known virtual environments, making malware analysis very difficult. Emulab developed by Utah University assigns real systems and networks as researchers want in realtime. This research seeks how to use Emulab for malware analysis.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.139-144
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• 2016

There exist many threats in cyber space, however current anti-virus software and other existing solutions do not effectively respond to malware that has become more complex and sophisticated. It was shown experimentally that it is possible for the proposed approach to provide an automatic execution environment for the detection of malicious behavior of active malware, comparing the virtual-machine environment with the real-machine environment based on user interaction. Moreover, the results show that it is possible to provide a dynamic analysis environment in order to analyze the intelligent malware effectively, through the comparison of malicious behavior activity in an automatic binary execution environment based on real-machines and the malicious behavior activity in a virtual-machine environment.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.113-131
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• 2014

Smartphone security threat has become an important issue in Information Science field following the wide distribution of smartphones. However, there are few studies related to such. Therefore, this study examined the factors affecting the intention of smartphone users to use the mobile vaccine against malware with the Protection Motivation Theory. To secure the reliability of the study, a surveying agency was commissioned. A total of 263 respondents, excluding 37 respondents who are users of iOS, which does not have mobile vaccine in the smart phone, or who gave invalid responses, were surveyed. The results showed that perception of the installed mobile vaccine significantly affected the Response Efficacy and Self-efficacy, and that the Perceived Severity, Perceived Vulnerability, Response Efficacy, and Self-efficacy significantly influenced the intention to use the mobile vaccine. On the other hand, Installation Perception of mobile vaccine itself did not affect the Perceived Severity and Perceived Vulnerability. This study is significant since it presented the new evaluation model of threat evaluation and response evaluation in the Protection Motivation Theory in accepting the security technology and raised the need for the promotion and exposure of mobile vaccine, since perception of mobile vaccine installation affects the response evaluation. It also found that the promotion must consider the seriousness of smartphone security, outstanding attribute of mobile vaccine, and user-friendliness of mobile vaccine above all.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.47-54
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• 2014

Anti-debugging is one of the techniques implemented within the computer code to hinder attempts at reverse engineering so that attackers or analyzers will not be able to use debuggers to analyze the program. The technique has been applied to various programs and is still commonly used in order to prevent malware or malicious code attacks or to protect the programs from being analyzed. In this paper, we will suggest an automatic detection scheme for anti-debugging routines. With respect to the automatic detection, debuggers and a simulator were used by which trace information on the Application Program Interface(API) as well as executive instructions were extracted. Subsequently, the extracted instructions were examined and compared so as to detect points automatically where suspicious activity was captured as anti-debugging routines. Based on experiments to detect anti-debugging routines using such methods, 21 out of 25 anti-debugging techniques introduced in this paper appear to be able to detect anti-debugging routines properly. The technique in the paper is therefore not dependent upon a certain anti-debugging method. As such, the detection technique is expected to also be available for anti-debugging techniques that will be developed or discovered in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.193-204
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• 2017

The security of Internet systems critically depends on the capability to keep anti-virus (AV) software up-to-date and maintain high detection accuracy against new malware. However, malware variants evolve so quickly they cannot be detected by conventional signature-based detection. In this paper, we proposed a malware classification method based on sequence patterns generated from the network flow of malware samples. We evaluated our method with 766 malware samples and obtained a classification accuracy of approximately 40.4%. In this study, malicious codes were classified only by network behavior of malicious codes, excluding codes and other characteristics. Therefore, this study is expected to be further developed in the future. Also, we can predict the attack groups and additional attacks can be prevented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).