• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.93-103
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• 2005

In this paper, we propose a RST resistant robust logo embedding technique for multimedia copyright protection Geometric manipulations are challenging attacks in that they do not introduce the quality degradation very much but make the detection process very complex and difficult. Watermark embedding in the normalized image directly suffers from smoothing effect due to the interpolation during the image normalization. This can be avoided by estimating the transform parameters using an image normalization technique, instead of embedding in the normalized image. Conventional RST resistant schemes that use full frame transform suffer from the absence of effective perceptual masking methods. Thus, we adopt $8\times8$ block DCT and calculate masking using a spatio-frequency localization of the $8\times8$ block DCT coefficients. Simulation results show that the proposed algorithm is robust against various signal processing techniques, compression and geometrical manipulations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.33-43
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• 2006

In order to provide the efficient personalized services, the organizations and the companies collect and manage the personal information. However, there have been increasing privacy concerns since the personal information might be misused and spread over in public by the database administrators or the information users. Even in the systems in which organizations or companies control access to personal information according to their access policy in order to protect personal information, it is not easy to fully reflect the information subjects' intention on the access control to their own Personal information. This paper proposes a policy-based access control mechanism for the personal information which prevents unauthorized information users from illegally accessing the personal information and enables the information subjects to control access over their own information. In the proposed mechanism, the individuals' personal information which is encrypted with different keys is stored into the directory repository. For the access control, information subjects set up their own access control policy for their personal information and the policies are used to provide legal information users with the access keys.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.111-121
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• 2006

As Internet lastly grows, network attack techniques are transformed and new attack types are appearing. The existing network-based intrusion detection systems detect well known attack, but the false-positive or false-negative against unknown attack is appearing high. In addition, The existing network-based intrusion detection systems is difficult to real time detection against a large network pack data in the network and to response and recognition against new attack type. Therefore, it requires method to heighten the detection rate about a various large dataset and to reduce the false-positive. In this paper, we propose method to reduce the false-positive using multi-level detection algorithm, that is combine the multidimensional Apriori algorithm and the modified Negative Selection algorithm. And we apply this algorithm in intrusion detection and, to be sure, it has a good performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.141-148
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• 2011

Recently, various social network services have been grown. Among them, personal relationships based social network services such as Facebook and Twitter make a remarkable growth of industry. In such services, users' profiles are very important for establishing the relationship between two users. However some information in a user's profile causes the leakage of the user's privacy, and thus we have to deal with the information in the profile. Especially, we have to treat contact information, such as the phone number and the e-mail address, very carefully since an adversary can use the information to violate the user's privacy in real life. In this paper, we propose two profile management systems that can enhance the privacy of users in social network services. We compare our systems with existing profile management techniques in well-known social network services such as Facebook and Twitter, and show that our systems provide enhanced privacy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.6
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• pp.71-82
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• 2009

There have been a lot of researches on providing privacy in MANET (Mobile Ad hoc NETwork) using trapdoor, onion, and anonymous authentication. Privacy protection in MANET can be divided into satisfying ID privacy, location privacy, route privacy, and unlinkability between sessions. Most of the previous works, however, were unsatisfactory with respect to location privacy or route privacy. Moreover, in previous schemes, cryptographic operation cost needed to meet the privacy requirements was relatively high. In this paper, we propose a new efficient anonymous routing protocol that satisfies all the privacy requirements and reduces operation costs. The proposed scheme does not use onion or anonymous authentication techniques in providing privacy. We also provide a more accurate analysis of our scheme's efficiency by considering all the nodes involved in the route establishment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.3
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• pp.23-30
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• 2010

Compression techniques can be applied to large-scale fingerprint systems to store or transmit fingerprint data efficiently. In this paper, we investigate the effects of FBI WSQ fingerprint image compression on the performance of a fingerprint verification system using multiple linear regressions. We propose a maximum compression using fingerprint image quality score. Based on the experiments, we can confirm that the proposed approach can compress the fingerprint images up to 3 times more than the fixed compression ratio without significant degradation of the verification accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.3
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• pp.31-41
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• 2010

It is difficult to identify attacker's real location when the attacker spoofs IP address in current IPv4-based Internet environment. If the attacks such as DDoS happen in the Internet, we can hardly expect the protection scheme to respond to these attacks in active or real-time manner. Many traceback techniques have been proposed to protect against these attacks, but most traceback schemes were designed to work with the IPv4-based Internet and found to be lack of verification of whether the traceback related information is forged or not. Few traceback schemes for IPv6-based network environment have been suggested, but it has these disadvantages that needs more study. In this paper, we propose the reliable IP traceback scheme supporting integrity of traceback-related information in IPv6 network environment, simulate it, and compare our proposed scheme with exsisting traceback mechanisms in terms of overhead and functionality.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.1
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• pp.17-31
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• 2010

In VANET (Vehicular Ad hoc NETwork), vehicles can efficiently verify a large number of signatures efficiently using batch verification techniques. However, batch verification performed independently in each vehicle raises many redundant verification cost. Although, an RSU (Road Side Unit) can perform the batch verification as a proxy to reduce this cost, it additionally requires an efficient method to identify invalid signatures when the batch verification fails. In this paper, we analyze several ways of constructing a distributed batch verification system, and propose an efficient distributed batch verification system in which participating vehicles perform batch verification in a distributive manner for a small size signature set. In our proposed system, each node can report the batch verification result or the identified invalid signatures list and the RSU who received these reports can identify the invalid signatures and efficiently exclude them.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.