• Information Systems Review
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• v.9 no.1
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• pp.121-137
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• 2007

Several security systems(firewall, intrusion detection system, vaccine for malicious codes and so on), whose purposes are to prevent the external information security threat, have gathered more technological concerns. However, they are little effective for the area of defending the internal information security threat which occurs more frequently and results in much more monetary damages. In this paper, a system for internal surveillance and control on the use of information systems is suggested and described with its architecture, features, necessary functions and development methods. And a case system is introduced to show the reality of this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.449-456
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• 2018

The advent of cryptography has become a means of obtaining real monetary benefits to hackers, which has recently led to a surge in the number of Ransomware and the associated damage has increased significantly. It is expected that malicious codes will be expanded to new areas by meeting passwords, and Ransomware will be further increased in the future. To solve these problems, we need a model that can detect and block intrusion of Ransomware by analyzing the intrusion path of Ransomware. In this paper, we collect and analyze the data of Ransomware, and create and analyze Ransomware's color Petri net model.

• Convergence Security Journal
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• v.15 no.1
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• pp.69-82
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• 2015

This dissertation introduce how to react against the cybercrime and analysis of malware detection. Also this dissertation emphasize the importance about efficient control of correspond process for the information security. Cybercrime and cyber breach are becoming increasingly intelligent and sophisticated. To correspond those crimes, the strategy of defense need change soft kill to hard kill. So this dissertation includes the study of weak point about OS, Application system. Also this dissertation suggest that API structure for handling and analyzing big data forensic.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.839-846
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• 2005

Recently broadband convergence network technology is emerging as an integrated network of telecommunication, broadcasting and Internet. But there are various threats as side effects against the growth of information technology, and malicious codes such af Internet worms may bring about confusions to upset a national backbone network. In this paper, we survey the traditional spreading models and propose a new worm spreading model on Internet environment. We also analyze the spreading effects due to tile spread period and the response period of Internet worms. The proposed model leads to a better prediction of the scale and speed of worm spreading. It can be applied to high-speed network such as broadband convergence network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5144-5160
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• 2019

Secret sharing is an effective way of protecting secret messages. However, the traditional secret sharing schemes are considered meaningless due to malicious people attention which might raise risks. To overcome the weakness, this paper presents an effective secret sharing scheme with the functionality of cheater identification, based on meaningful QR code. The secret message will be split and concealed in the padding region of cover QR codes with the assistance of Latin square and it can be completely restored when all the involved participants cooperate. The concealing strategy exploits the characteristic of Reed-Solomon (RS) code to ensure the strong robustness of generated QR code pseudo-shares. The meaningful QR code pseudo-shares help to reduce the curious of unrelated persons. Some experiments were done to evaluate the performance of proposed scheme. The results showed that the proposed scheme is feasible, efficient and secure compared to the other existing schemes. It also achieves a higher secret payload and maintains stronger robustness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.51-58
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• 2020

The use of malware in cyber threats continues to be used in all ages, and will continue to be a major attack method even if IT technology advances. Therefore, researches for detecting such malicious codes are constantly tried in various ways. Recently, with the development of AI-related technology, many researches related to machine learning have been conducted to detect malware. In this paper, we propose a method to detect malware using machine learning. For machine learning detection, we create a feature around each call interval, ie Time Interval, in which API calls occur among dynamic analysis data, and then apply the result to machine learning techniques.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.77-87
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• 2019

One of important concerns in information security is to control information flow. It is whether to protect confidential information from being leaked, or to protect trusted information from being tainted. In this paper, we present Piosk (Physical blockage of Information flow Kiosk) that addresses both the problems practically. Piosk can forestall and prevent the leakage of information, and defend inner tangible assets against a variety of malwares as well. When a visitor who carries a re-writable portable storage device, must insert the device into Piosk installed next to the security gate. Then, Piosk scans the device at the very moment, and detects & repairs malicious codes that might be exist. After that, Piosk writes the contents (including sanitized ones) on a new read-only portable device such as a compact disk. By doing so, the leakage of internal information through both insiders and outsiders can be prevented physically. We have designed and prototyped Piosk. The experimental verification of the Piosk prototype implementation reveals that, Piosk can accurately detect every malware at the same detection level as Virus Total and effectively prevent the leakage of internal information. In addition, we compare Piosk with the state-of-the-art methods and describe the special advantages of Piosk over existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.947-950
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• 2014

The recent APT attacks including cyber terror are caused by a high level of malicious codes and hacking techniques. This implies that essentially, advanced security management is required, from the perspective of 5A. The changes of IT environment are represented by Mobile, Cloud and BYOD. In this situation, the security model needs to be changed, too into the Airport model which emphasizes prevention, and connection, security and integration of functions from the existing Castle model. This study suggested an application method of the risk-based Airport model to the cyber security environment.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.9-19
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• 2019

Service and users over the Internet are increasing rapidly. Cyber attacks are also increasing. As a result, information leakage and financial damage are occurring. Government, public agencies, and companies are using security systems that use signature-based detection rules to respond to known malicious codes. However, it takes a long time to generate and validate signature-based detection rules. In this paper, we propose and develop signature based detection rule generation and verification systems using the signature extraction scheme developed based on the LDA(latent Dirichlet allocation) algorithm and the traffic analysis technique. Experimental results show that detection rules are generated and verified much more quickly than before.