• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.89-98
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• 2007

We have come a long way in the information age. Thanks to the advancement of such technologies as the internet, we have discovered new ways to convey information on a broader scope. However, negative aspects exist as is with anything else. These may include invasion of privacy over the web, or identity theft over the internet. What is more alarming is that malwares so called 'maliciouscodes' are rapidly spreading. Its intent is very destructive which can result in hacking, phishing and as aforementioned, one of the most disturbing problems on the net, invasion of privacy. This thesis describes the technology of how you can effectively analyze and detect these kind of malicious codes. We propose sequencial hybrid analysis for API calls that are hooked inside user-mode and kernel-level of Windows. This research explains how we can cope with malicious code more efficiently by abstracting malicious function signature and hiding attribute.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.51-61
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• 2009

Recently, the cyber-attacks using botnets are being increased, Because these attacks pursue the money, the criminal aspect is also being increased, There are spreading of spam mail, DDoS(Distributed Denial of Service) attacks, propagations of malicious codes and malwares, phishings. leaks of sensitive informations as cyber-attacks that used botnets. There are many studies about detection and mitigation techniques against centralized botnets, namely IRC and HITP botnets. However, P2P botnets are still in an early stage of their studies. In this paper, we analyzed the traffics of the Peacomm bot that is one of P2P-based storm bot by using honeynet which is utilized in active analysis of network attacks. As a result, we could see that the Peacomm bot sends a large number of UDP packets to the zombies in wide network through P2P. Furthermore, we could know that the Peacomm bot makes the scale of botnet maintained and extended through these results. We expect that these results are used as a basis of detection and mitigation techniques against P2P botnets.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.179-188
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• 2012

Packing techniques, one of malicious code detection and analysis avoidance techniques, change code to reduce size and make analysts confused. Therefore, malwares have more time to spread out and it takes longer time to analyze them. Thus, these kind of unpacking techniques have been studied to deal with packed malicious code lately. Packed programs are unpacked during execution. When it is unpacked, the data inside of the packed program are changed. Because of these changes, the entropy value of packed program is changed. After unpacking, there will be no data changes; thus, the entropy value is not changed anymore. Therefore, packed programs could be unpacked finding the unpacking point using this characteristic regardless of packing algorithms. This paper suggests the generic unpacking mechanism using the method estimating the unpacking point through the variation of entropy values.

• 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).