• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.9-17
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• 2009

RFID (Radio Frequency Identification) is a next generation technology that will replace barcode. RFID can identify an object by reading ID inside a RFID tag using radio frequency. However, because a RFID tag replies its unique ID to the request of any reader through wireless communication, it is vulnerable to attacks on security or privacy through wiretapping or an illegal reader's request. The RFID authentication protocol has been studied actively in order to solve security and privacy problems, and is used also in tag search. Recently, as the number of tags is increasing in RFTD systems and the cost of data collection is also rising, the importance of effective tag search is increasing. This study proposed an efficient search method that solved through ta9 group the problem of large volume of database computation in Miyako Ohkubo's hash chain mechanism, which meets requirements for security and privacy protection. When we searched first the group of tags with access rate of 5 or higher in a database with 100,000 records, search time decreased by around 30%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.67-80
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• 2012

Recently as the number of smartphone user is growing rapidly, android is also getting more interest in digital forensic. However, there is not enough research on digital data acquisition and analysis based on android platform's unique characteristics so far. Android system stores all the related recent systemwide logs from the system components to applications in volatile memory, and therefore, the logs can potentially serve as important evidences. In this paper, we propose a digital data acquisition and analysis system for android which extracts meaningful information based on the correlation of android logs and user activities from a device at runtime. We also present an efficient search scheme to facilitate realtime analysis on site. Finally, we demonstrate how the proposed system can be used to reconstruct the sequence of user activities in a more intuitive manner, and show that the proposed search scheme can reduce overall search and analysis time approximately 10 times shorter than the normal regular search method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.273-283
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• 2021

The aim of this paper is to review the current knowledge management (KM) practices and their importance in the public sector of Saudi Arabia. Using the term "knowledge management in public sector", a search for available works was done in Google Scholar. In the first stage, the search for literature published under anytime choice was done in the first 10 pages of the search engine. Then another nine pages of Goggle Scholar were searched for more recent works published setting the time as 2014 to 2018. The search yielded 37 usable works for this review. The results of this literature search and review indicated that out of the 37 works reviewed, only three works were related to Saudi Arabia. Number of papers on factors of KM were maximum, many of them giving diagrammatic presentation of their results. KM modelling itself is not easy as only very few papers on KM modelling were available. Problems of too much reliance on qualitative data and hypotheses not matching with the literature backgrounds for them were also found. Considering the works related to KM, the number of papers in various categories may indicate the dimensions of KM to be considered when KM is implemented or evaluated in any public sector of any country. This applies to Saudi Arabian public sector organizations also. There is a fertile ground of research waiting to be investigated by researchers in Saudi Arabia.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.157-162
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• 2014

The international COSPAS-SARSAT program is a satellite-based search and rescue distress alert detection and information distribution system and best known as the system that detects and locates emergency beacons activated by aircraft, ships and so on. However, the current message format of the system is not encrypted so that, if the rescue signal can be intercepted by the unintended receivers, the subsequent rescue activities can be handled in a hostile environment. So, this article concerns how to deal with the rescue signals in a secure way and proposes some adequate encryption methods and the corresponding key management.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1658-1666
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• 2009

This paper proposes an approach of Mult_HPSO based on the PC cluster system to reduce or remove the stagnation on an early convergence effect of PSO, reduce an execution time and improve a search ability on an optimal solution. Hybrid PSO(HPSO) is combines the PSO(Particle Swarm Optimization) with the mutation of conventional GA(Genetic Algorithm). The conventional PSO has operated a search process in a single swarm. However, Multi_PSO operates a search process through multiple swarms, which increments diversity of expected solutions and reduces the execution time. Multiple Swarms are composed of unsynchronized PC clusters. We apply to SCED(security constrained economic dispatch) problem, a nonlinear optimization problem, which considers line flow constraints and N-1 line contingency constraints. To consider N-1 line contingency in power system, we have chosen critical line contingency through a process of Screening and Selection based on PI(performace Index). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed approaches.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.65-74
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• 2021

Digital image processing and retrieving have increasingly become very popular on the Internet and getting more attention from various multimedia fields. That results in additional privacy requirements placed on efficient image matching techniques in various applications. Hence, several searching methods have been developed when confidential images are used in image matching between pairs of security agencies, most of these search methods either limited by its cost or precision. This study proposes a secure and efficient method that preserves image privacy and confidentially between two communicating parties. To retrieve an image, feature vector is extracted from the given query image, and then the similarities with the stored database images features vector are calculated to retrieve the matched images based on an indexing scheme and matching strategy. We used a secure content-based image retrieval features detector algorithm called Speeded-Up Robust Features (SURF) algorithm over public cloud to extract the features and the Honey Encryption algorithm. The purpose of using the encrypted images database is to provide an accurate searching through encrypted documents without needing decryption. Progress in this area helps protect the privacy of sensitive data stored on the cloud. The experimental results (conducted on a well-known image-set) show that the performance of the proposed methodology achieved a noticeable enhancement level in terms of precision, recall, F-Measure, and execution time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.125-134
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• 2008

A great number of RFID authentication protocols have been proposed for the secure RFID system. These are typically divided into three types according to primitive that they use : Hash-based, Re-encryption based, and XORing-based protocol. The well-known attacks in RFID system are eavesdropping. impersonating, location tracking, and so on. However, existing protocols could not provide security against above attacks, or it was not efficient to search for tags on database. Therefore, in this paper we present a protocol which is secure against above attacks by using hash function and makes Database search tags easily by attaining the state information of previous session through the shared values with all tags and database.