• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.901-909
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• 2021

Since mobile devices have limited computational resources, it tends to use the cloud to compute or store data. As real-time becomes more important due to 5G, many studies have been conducted on edge clouds that computes at locations closer to users than central clouds. The farther the user's physical distance from the edge cloud connected to base station is, the slower the network transmits. So applications should be migrated and re-run to nearby edge cloud for smooth service use. We run applications in docker containers, which is independent of the host operating system and has a relatively light images size compared to the virtual machine. Existing migration studies have been experimented by using network simulators. It uses fixed values, so it is different from the results in the real-world environment. In addition, the method of migrating images through shared storage was used, which poses a risk of packet content exposure. In this paper, Containers are migrated with Secure CoPy(SCP) method, a data encryption transmission, by establishing an edge computing environment in a real-world environment. It compares migration time with Network File System, one of the shared storage methods, and analyzes network packets to verify safety.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6849-6855
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• 2014

Recently, with the increasing use of smart phones, security issues, such as safety and reliability of the use of the Android application has become a topic to provide services in various forms. An Android application is performed using several important files in the form of an apk file. On the other hand, they may be subject to unauthorized use, such as the loss of rights and privileges due to the insertion of malicious source code of these apk files. This paper examines the Android environment to study ways to define the threats related to the unauthorized use of the application source code, and based on the results of the analysis, to prevent unauthorized use of the application source code. In this paper, a system is provided using a third body to prevent and detect applications that have been counterfeited or forged illegally and installed on Android devices. The application provides services to existing systems that are configured with only the service server that provides users and applications general, This paper proposes the use of a trusted third party for user registration and to verify the integrity of the application, add an institution, and provide a safe application.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.15-22
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• 2018

As smart devices become popular, users can use authentication services in various methods. Authentication services include authentication using an ID and a password, authentication using a sms, and authentication using an OTP(One Time Password). This paper proposed an authentication system that solves the security problem of knowledge-based authentication using optical character recognition and can easily and quickly authenticate users. The proposed authentication system extracts a character from an uploaded image by a user and authenticates the user using the extracted character information. The proposed authentication system has the advantage of not using a password or an OTP that are easily exposed or lost, and can not be authenticated without using accurate photographs. The proposed authentication system is platform independent and can be used for user authentication, file encryption and decryption.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.1048-1058
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• 2012

This paper proposes the DDMPF(Distributed Data Management Protocol using FAT) which prevents data loss and keeps the security of self-organized storages by comprising a client, a storage server, and a verification server in clouding environment. The DDMPF builds a self-organized storage server, solves data loss by decentralizing the partitioned data in it in contrast to the centralized problem and the data loss caused by the storage server problems of existing clouding storages, and improves the efficiency of distributed data management with FAT(File Allocation Table). And, the DDMPF improves the reliability of data by a verification server's verifying the data integrity of a storage server, and strengthens the security in double encryption with a client's private key and the system's master key using EC-DH algorithm. Additionally, the DDMPF limits the number of verification servers and detects the flooding attack by setting the TS(Time Stamp) for a verification request message and the replay attack by using the nonce value generated newly, whenever the verification is requested.

• Journal of Broadcast Engineering
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• v.15 no.2
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• pp.192-204
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• 2010

As illegal distribution of the terrestial DTV broadcast program occurs very frequently in on-line, the needs to protect broadcast program have increased. In this paper, a new approach to implement a system for terrestial DTV broadcast program protection based on program protection information(PPI) is proposed. In our approach, the broadcast program is recorded with encryption according to redistribution condition of the PPI and packaged into a file with key information and PPI together. And we also define a set of domain protocol for supporting user fair-use of broadcast program. In the proposed system, copy control can also be provided by process of home domain management. Implementation results show that our system can protect broadcast programs with efficiency and can support conditional distribution within home domain in order to satisfy user fair-use.

• Journal of Platform Technology
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• v.8 no.3
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• pp.3-9
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• 2020

As the need for sharing for research purposes in the medical field increases, the use of a Common Data Model (CDM) is increasing. However, when sharing CDM data, there are some problems in that access control and personal information in the data are not protected. In this paper, in order to solve this problem, access to CDM data is controlled by using an encryption method in a blockchain network, and information of CDM data is recorded to enable tracking. In addition, IPFS was used to share a large amount of CDM data, and Celery was used to automate the sharing process. In other words, we propose a multi-channel automation system in which the information required for CDM data sharing is shared by a trust-based technology, a distributed file system, and a message queue for automation. This aims to solve the problem of access control and personal information protection in the data that occur in the process of sharing CDM data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.205-214
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• 2012

IDPF, which builds formats and copyright protection standards of eBooks, has announced ePUB 3.0 as a technical standard of eBooks in October, 2011. This standard includes methods how to represent eBooks and technical specifications to protect eBook content. While technical specifications for content protection describe how to represent encryption and digital signature techniques, they do not identify any technical standards for rights expression but just file names for storages of rights information. It does not provide any unification of copyright information representation and formats used by eBook service companies. When copyright protection techniques for eBooks are used, comparability among eBook readers cannot be expected, even though there is a standard of ePUB. This study suggests a method to maintain compatibility toward eBook DRM by using unified rights information process under circumstances where different eBook service companies use diverse methods. The standard reference software of the model proposed in this study, together with other results of this study, will be offered as a registered open software.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.21-31
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• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• 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.