• Journal of KIISE
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• v.42 no.5
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• pp.652-663
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• 2015

Due to the popularity of location-based services, the amount of generated spatial data in daily life has been dramatically increasing. Therefore, spatial database outsourcing has become popular for data owners to reduce the spatial database management cost. The most important consideration in database outsourcing is meeting the privacy requirements and guarantying the integrity of the query result. However, most of existing database transformation techniques do not support both of the data privacy and integrity of the query result. To solve this problem, we propose a spatial data transformation scheme that utilizes the shearing transformation with rotation shifting. In addition, we described the attack models to measure the data privacy of database transformation schemes. Finally, we demonstrated through the experimental evaluations that our scheme provides high level of data protection against different kinds of attack models, compared to the existing schemes, while guaranteeing the integrity of the query result sets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.975-986
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• 2022

Deep learning is attracting great attention, showing excellent performance in image processing, but is vulnerable to adversarial attacks that cause the model to misclassify through perturbation on input data. Adversarial examples generated by adversarial attacks are minimally perturbated where it is difficult to identify, so visual features of the images are not generally changed. Unlikely deep learning models, people are not fooled by adversarial examples, because they classify the images based on such visual features of images. This paper proposes adversarial attack detection method using Symbolic Representation, which is a visual and symbolic features such as color, shape of the image. We detect a adversarial examples by comparing the converted Symbolic Representation from the classification results for the input image and Symbolic Representation extracted from the input images. As a result of measuring performance on adversarial examples by various attack method, detection rates differed depending on attack targets and methods, but was up to 99.02% for specific target attack.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.288-299
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• 2012

To ensure data confidentiality and fine-grained access control in business environment, system model using KP-ABE(Key Policy-Attribute Based Encryption) and PRE(Proxy Re-Encryption) has been proposed recently. However, in previous study, data confidentiality has been effected by decryption right concentrated on cloud server. Also, Yu's work does not consider a access privilege management, so existing work become dangerous to collusion attack between malicious user and cloud server. To resolve this problem, we propose secure system model against collusion attack through dividing data file into header which is sent to privilege manager group and body which is sent to cloud server and prevent modification attack for proxy re-encryption key using d Secret Sharing, We construct protocol model in medical environment.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.213-216
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• 2011

Differential Power Analysis is fully affected by various noises including temporal misalignment. Recently, Ryoo et al have introduced an efficient preprocessor method leading to improvements in DPA by removing the noise signals. This paper experimentally proves that the existing preprocessor method is not applied to all processor. To overcome this defect, we propose a Differential Trace Model(DTM). Also, we theoretically prove and experimentally confirm that the proposed DTM suites DPA.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.141-150
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• 2007

This paper proposes new Steganographic model for VoIP that has very effective method using low bit encoding. Most of Steganographic models using Low bit Encoding have two disadvantages; one is that the existence of hidden secret message can be easily detected by auditory, the other is that the capacity of stego data is low. To solve these problems, this method embed more than one bit in inaudible range, so this method can improve the capacity of the hidden message in cover data. The embedding bit position is determined by using a pseudo random number generator which has seed with remaining message length, so it is hard to detect the stego data produced by the proposed method. This proposed model is able to use not only to communicate wave file with hidden message in VoIP environment but also to hide vary information which is user basic information, authentication system, etc.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.491-496
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• 2003

We present a new watermark design tool for digital images and digital videos that are based on human visual system (HVS) characteristics. In this tool, basic mechanisms (inhibitory and excitatory behaviour of cells) of HVS are used to determine image dependent upper bound values on watermark insertion. This allows us to insert maximai allowable transparent watermark, which in turn is extremely hard to attack with common image processing, Motion Picture Experts Group (MPEG) compression. As the number of details (e.g. edges) increases in an image, the HVS decrease its sensitivity to the details. In the same manner, as the number of motion increases in a video signal, the HVS decrease its sensitivity to the motions. We model this decreased sensitivity to the details and motions as an (motion) entropy masking. Entropy masking model can be efficiently used to increase the robustness of image and video watermarks. We have shown that our entropy-masking model provides watermark scheme with increased transparency and henceforth increased robustness.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.104-112
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• 2022

The pandemic caused by the COVID-19 virus, which has lasted for the past three years, has changed society and the way people live in many ways. These changes also affect cyberspace, so the pre-pandemic information security model and standards have limitations when applied to the current situation. In this paper, a new method to improve the information security model of public institutions was proposed in consideration of various situations in the new normal era. In other words, through the proposed information security model, the possibility of external intrusion is blocked in advance through the policy and technical supplementation of remote work, which is a weakness of the existing information security operation of public institutions. Also, how to prevent abnormal authentication attempts by building a secure VPN environment, how to prevent social engineering cyber attacks targeting fear and uncertainty caused by COVID-19, and how to use a smooth network and create a remote work environment. For this purpose, methods for securing service availability were additionally presented.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Annual Conference of KIPS
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• 2011.11a
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• pp.941-944
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• 2011

모바일 클라우드 서비스는 사용자가 모바일 단말에 자원을 가지고 있지 않더라도 인터넷을 통해 외부의 다양한 IT 자원을 제공하는 서비스로서 모바일 단말이 가지는 성능적 한계를 극복시킬 수 있다는 장점과 함께 이용자 수가 증가하고 있다. 그러나 클라우드 컴퓨팅 환경에 존재하는 개인 및 기업의 정보 유출과 같은 문제들은 모바일 클라우드 컴퓨팅 환경에도 그대로 상속되기 때문에 이러한 문제에 대응하기 위해서는 모바일 클라우드 컴퓨팅 환경에서 정보유출을 탐지할 수 있는 이상행위 탐지 알고리즘이 마련되어야 한다. 여기서 이상행위란, 모바일 클라우드 자원에 접근하는 방법에 있어 기존에 인지하고 있던 정상적인 행위에서 벗어나는 행위를 의미하며 이상행위로 판단되는 상황이 발생되는 경우, 이를 정보유출이 발생할 수 있는 상황으로 인지함으로써 적절한 대응을 할 수 있게 된다. 따라서 본 논문에서는 모바일 클라우드 자원의 정보유출을 방지하기 위한 목적으로 자원 접근에 대한 이상행위 탐지 알고리즘 개발 모델을 제시한다. 이상행위 탐지 알고리즘을 개발하고 이를 검증하기 위해서는 이상행위를 일으키는 공격 모델 및 대응 모델이 개발되어야 한다. 따라서 본 논문에서는 인증 및 권한관리의 취약점을 이용하여 위협을 일으키는 공격 모델을 개발하는 방법을 제시하고, 사용자의 접속환경 및 클라우드 자원의 정보 흐름을 분석함으로써 이상행위를 탐지하는 알고리즘을 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.122-128
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• 2000

With the rising innovative antigens(such as intruders and viruses) through Internet, new secure schemes are expected to perceptively detect and put them down. However, the current hosts over Internet could not properly analyze Internet antigens due to limitations of their system and network resources. In this paper, we introduce an Antibody Layer that mediates proper security services based on the biological mechanism to mpidly disclose and remove innovative antigens. The proposed Antibody Layer also provides three classes to make agreed-on security parameters set up easily with respect to real-time security QoS for one host as well as host alliances.