• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.1-9
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• 2013

This paper propose a human action recognition method that uses bag-of-features (BoF) based on CS-LBP (center-symmetric local binary pattern) and a spatial pyramid in addition to the random forest classifier. To construct the BoF, an image divided into dense regular grids and extract from each patch. A code word which is a visual vocabulary, is formed by k-means clustering of a random subset of patches. For enhanced action discrimination, local BoF histogram from three subdivided levels of a spatial pyramid is estimated, and a weighted BoF histogram is generated by concatenating the local histograms. For action classification, a random forest, which is an ensemble of decision trees, is built to model the distribution of each action class. The random forest combined with the weighted BoF histogram is successfully applied to Standford Action 40 including various human action images, and its classification performance is better than that of other methods. Furthermore, the proposed method allows action recognition to be performed in near real-time.

• Journal of the Korean Institute of Telematics and Electronics
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• v.18 no.5
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• pp.1-9
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• 1981

Experimental results on a 4-bit charge-coupled A/D converter are described. Major operations in the successive approximation algorithm are implemented in a monolithic chip, CCADC, which was fabricated usir p-channel CCD technology, with its die size of 4,200 mil2 Typical operating frequency range has been found out to be from 500Hz to 200kHz. In that frequency range, no missing code has been found in the whole signal range of 2.4 volts for ramp signal slewing at 1 LSB/(sampling time). A discussion is made on several layout techniques to conserve the nominal binary ratio of (8:4:2:1) among the areas of four adjacent potential wells (M wells), whose charge storing capacities correspond to each bit magnitude - 3.6 pC, 1.8 pC, 0.9 pC, and 0.45 pC nominal in the order of MSB to the LSB. The effect of 'dump slot', which is responsible for the excessive nonlinearity (2$\frac{1}{2}$LSB) in the A/D converter, is explained. A novel input scheme called 'slot zero insertion' to circumvent the deleterious effects of the dump slot is described with the experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.741-743
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• 2017

The visible light communication is a communication method of outputting binary data based on the illumination threshold value at the light receiving diode of the receiving unit, when the LED of the transmitting unit transmits light. However, separate receiver is necessary to receive the optical signal, there is a problem that a device not equipped with such a receiver can not utilize visible light communication. To solve this problem, this paper proposes a portable visible light receiver applicable to devices using USB OTG. Implemented portable visible light receiver converts the binary data received from the LED into a character string of ASCII code and transmits it to another device. Through data transmission experiments using smart phone confirmed that it is possible to transmit ASCII codes in the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1013-1021
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• 2020

Malware Authorship Attribution is a research field for identifying malware by comparing the author characteristics of unknown malware with the characteristics of known malware authors. The authorship attribution method using binaries has the advantage that it is easy to collect and analyze targeted malicious codes, but the scope of using features is limited compared to the method using source code. This limitation has the disadvantage that accuracy decreases for a large number of authors. This study proposes a method of 'Defining semantic features from binaries' and 'Defining allowable ranges for redundant features using the concept of survival network' to complement the limitations in the identification of binary authors. The proposed method defines Opcode-based graph features from binary information, and defines the allowable range for selecting unique features for each author using the concept of a survival network. Through this, it was possible to define the feature definition and feature selection method for each author as a single technology, and through the experiment, it was confirmed that it was possible to derive the same level of accuracy as the source code-based analysis with an improvement of 5.0% accuracy compared to the previous study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.785-802
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• 2017

As cyber threats using malicious code continue to increase, many security and vaccine companies are putting a lot of effort into analysis and detection of malicious codes. However, obfuscation techniques that make software analysis more difficult are applied to malicious codes, making it difficult to respond quickly to malicious codes. In particular, commercial obfuscation tools can quickly and easily generate new variants of malicious codes so that malicious code analysts can not respond to them. In order for analysts to quickly analyze the actual malicious behavior of the new variants, reverse obfuscation(=de-obfuscation) is needed to disable obfuscation. In this paper, general analysis methodology is proposed to de-obfuscate the software used by a commercial obfuscation tool, Themida. First, We describe operation principle of Themida by analyzing obfuscated executable file using Themida. Next, We extract original code and data information of executable from obfuscated executable using Pintool, DBI(Dynamic Binary Instrumentation) framework, and explain the implementation results of automated analysis tool which can deobfuscate to original executable using the extracted original code and data information. Finally, We evaluate the performance of our automated analysis tool by comparing the original executable with the de-obfuscated executable.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1177-1183
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• 2018

Recent developments in machine learning have attracted a lot of attention for techniques such as machine learning and deep learning that implement artificial intelligence. In this paper, binary malicious code using deep learning based R-CNN is imaged and the feature is extracted from the image to classify the family. In this paper, two steps are used in deep learning to image malicious code using CNN. And classify the characteristics of the family of malicious codes using R-CNN. Generate malicious code as an image, extract features, classify the family, and automatically classify the evolution of malicious code. The detection rate of the proposed method is 93.4% and the accuracy is 98.6%. In addition, the CNN processing speed for image processing of malicious code is 23.3 ms, and the R-CNN processing speed is 4ms to classify one sample.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.537-545
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• 2011

This paper analyzed performance of three dimensional turbo code and turbo ${\Phi}$ codes proposed in the next generation DVB-RCS systems. In the view of turbo ${\Phi}$ codes, we proposed the optimal permutation and puncturing patterns for triple binary input data. We also proposed optimal post-encoder types and interleaving algorithm for three dimensional turbo codes. Based on optimal parameters, we simulated both turbo codes, and we confirmed that the performance of turbo ${\Phi}$ codes are better than that of three dimensional turbo codes. However, the complexity of turbo ${\Phi}$ is more complex than that of three dimensional turbo codes by 18%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10C
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• pp.1016-1023
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• 2009

In this paper, a tracking bias compensation method is proposed for future global navigation satellite systems (GNSSs). It is observed that the correlation function of a GNSS signal has many peaks and remains almost unchanged in the advanced offset region as a result of the multipath signals arriving at the receiver later than a line-of-sight signal. Based on these observations, we use the slopes in the advanced offset region to compensate for the code tracking bias, and obtain the maximum code tracking bias, which is essential to implement the proposed scheme, in static multipath environments. Finally, it is demonstrated that the proposed compensation method is very effective for the GNSS signal tracking in terms of the code tracking biases and their running averages.