• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.207-216
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• 2023

For decoding-compatible parallel Deflate algorithm, this study proposed a new method of the control header being made in such a way that essential information for parallel compression and decompression are stored in the Disposed Bit Area (DBA) of the non-compression block and being inserted into the compressed blocks. Through this, parallel compression and decompression are possible while maintaining perfect compatibility with the existing decoder. After applying this method, the compression time was reduced by up to 71.2% compared to the sequential processing method, and the parallel decompression time was reduced by up to 65.7%. In particular, it is well known that parallel decompression is impossible due to the structural limitations of the Deflate algorithm. However, the decoder equipped with the proposed method enables high-speed parallel decompression at the algorithm level and maintains compatibility, so that parallelly compressed data can be decoded normally by existing decoder programs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.869-879
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• 2022

Through this study, we discovered that among three types of compressed data blocks generated through the Deflate algorithm, No-Payload Non-Compressed Block type (NPNCB) which has no literal data can be randomly generated and inserted between normal compressed blocks. In the header of the non-compressed block, there is a data area that exists only for byte alignment, and we called this area as DBA (Disposed Bit Area), where an attacker can hide various malicious codes and data. Finally we found the vulnerability that hides malicious codes or arbitrary data through inserting NPNCBs with infected DBA between normal compressed blocks according to a pre-designed attack scenario. Experiments show that even though contaminated NPNCB blocks were inserted between normal compressed blocks, commercial programs decoded normally contaminated zip file without any warning, and malicious code could be executed by the malicious decoder.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1107-1115
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• 2017

The most commonly used PKZIP format is a ZIP file, as well as a file format used in MS Office files and application files for Android smartphones. PKZIP format files, which are widely used in various areas, require structural analysis from the viewpoint of digital forensics and should be able to recover when files are damaged. However, previous studies have focused only on recovering data or extracting meaningful data using the Deflate compression algorithm used in ZIP files. Although most of the data resides in compressed data in the ZIP file, there is also forensically meaningful data in the rest of the ZIP file, so you need to restore it to a normal ZIP file format. Therefore, this paper presents a technique to recover a damaged ZIP file to a normal ZIP file when given.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.231-238
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• 2006

In this paper, the method to make lossless image compression that holds considerable part of total volume of mobile game has been proposed. To increase the compression rate, we compress the image by Deflate algorithm defined in RFC 1951 after reorganize it at preprocessing stage before conducting actual compression. At the stage of preprocessing, we obtained the size of a dictionary based on the information of image which is the feature of Dictionary-Based Coding, and increased the better compression rate than compressing in a general manner using in a way of restructuring image by pixel packing method and DPCM prediction technique. It has shown that the method increased 9.7% of compression rate compare with existing mobile image format, after conducting the test of compression rate applying the suggested compression method into various mobile games.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.379-392
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• 2024

With the rapid increase in data, saving storage space and improving the efficiency of data transmission have become critical issues, making the research on the efficiency of data compression technologies increasingly important. Lossless algorithms can precisely restore original data but have limited compression ratios, whereas lossy algorithms provide higher compression rates at the expense of some data loss. There has been active research in data compression using deep learning-based algorithms, especially the autoencoder model. This study proposes a new side-channel analysis data compressor utilizing autoencoders. This compressor achieves higher compression rates than Deflate while maintaining the characteristics of side-channel data. The encoder, using locally connected layers, effectively preserves the temporal characteristics of side-channel data, and the decoder maintains fast decompression times with a multi-layer perceptron. Through correlation power analysis, the proposed compressor has been proven to compress data without losing the characteristics of side-channel data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.187-193
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• 2015

The ICT healing platform sets a couple of goals including preventing chronic diseases and sending out early disease warnings based on personal information such as bio-signals and life habits. The 2-step open system(TOS) had a relay designed between the healing platform and the storage of personal health data. It also took into account a publish/subscribe(pub/sub) service based on large-scale connections to transmit(monitor) the data processing process in real time. In the early design of TOS pub/sub, however, the same buffers were allocated regardless of connection idling and type of message in order to encode connection messages into a deflate algorithm. Proposed in this study, the dynamic buffer allocation was performed as follows: the message transmission type of each connection was first put to queuing; each queue was extracted for its feature, computed, and converted into vector through tf-idf, then being entered into a k-means cluster and forming a cluster; connections categorized under a certain cluster would re-allocate the resources according to the resource table of the cluster; the centroid of each cluster would select a queuing pattern to represent the cluster in advance and present it as a resource reference table(encoding efficiency by the buffer sizes); and the proposed design would perform trade-off between the calculation resources and the network bandwidth for cluster and feature calculations to efficiently allocate the encoding buffer resources of TOS to the network connections, thus contributing to the increased tps(number of real-time data processing and monitoring connections per unit hour) of TOS.