• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.197-202
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• 2022

IoT (Internet of Things) devices are being attacked by malware due to many security vulnerabilities, such as the use of weak IDs/passwords and unauthenticated firmware updates. However, due to the diversity of CPU architectures, it is difficult to set up a malware analysis environment and design features. In this paper, we design time series features using the byte sequence of executable files to represent independent features of CPU architectures, and analyze them using recurrent neural networks. The proposed feature is a fixed-length time series pattern extracted from the byte sequence by calculating partial entropy and applying linear interpolation. Temporary changes in the extracted feature are analyzed by RNN and LSTM. In the experiment, the IoT malware detection showed high performance, while low performance was analyzed in the malware family classification. When the entropy patterns for each malware family were compared visually, the Tsunami and Gafgyt families showed similar patterns, resulting in low performance. LSTM is more suitable than RNN for learning temporal changes in the proposed malware features.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.277-286
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• 2002

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.143-151
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• 2010

We carried out studies on InSAR techniques for time-series deformation monitoring using multi-temporal SAR. The PSInSAR method using permanent scatterer is much more complicate than the SBAS because it includes many non-linear equation due to the input of wrapped phase. It is conformed the PS algorithm is very sensitive to even PSC selection. On the other hand, the SBAS method using interferogram of small baseline subset is simple but sensitive to the accuracy of unwrapped phase. The SBAS is better method for expecting not significant unwrapping error while PSInSAR is more proper method for expecting local deformation within very limited area. We used 51 ERS-1/2 SAR data during 1992-2000 over Las Vegas, USA for the comparison between PSInSAR and SBAS. Both PSInSAR and SBAS show similar ground deformation value although local deformation seems to be detected in the PSInSAR method only.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.44-47
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• 2000

본 연구에서는 향이 인간에 미치는 영향을 EEG의 시계열 분석을 통해 알아보았다. 피험자는 20대 초반의 후각자애가 없는 30명(남녀 각각 15명)을 대상으로 하여, 국제 기준 전극법을 사용하여 Fz과 Cz에서 뇌파를 기록하였다. 100%의 Rose oil Bulgarian, Lemon oil Mistitano, Jasmine abs, Lavender oil France, Peppermint oil을 실험 시약으로 사용하였다. 각 향 자극에 대하여 1분 동안의 측정을 10초 간격으로 구분하여 $\alpha/(\alpha+\beta)$ 비와 $\beta/(\alpha+\beta)$ 대역의 비를 비교 분석하였다. 30초까지는 안정과 향 자극간의 차이가 증대되는 성향을 보였으나 50초부터는 감소되는 경향을 보였다. 본 연구를 통해 향간의 차이가 자극제시 후 30초 일 때 가장 큰 것으로 나타났다고 이 시간을 기준으로 각 향의 선호도를 분석하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.268-272
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• 2012

현재 확률강우량을 산정할 때는 수문사상 자료계열이 정상성을 가지고 있다고 가정하고 산정하고 있다. 이는 경향성 검정을 통과하지 못한 비정상성을 가지는 자료계열이라 할지라도 이들 자료에 대해 해석을 할 수 있는 검증된 대안이 아직 없기 때문이다. 따라서 본 연구에서는 강우의 증가경향성이 존재하여 경향성 검정을 통과하지 못한 비정상성을 가지는 지역에 대해서 경향성을 고려한 확률강우량을 산정하고, 기존의 방법에 의해서 산정된 확률강우량과 비교해보았다. 그리고 현재까지의 강우량 자료를 시계열분석을 이용하여 미래 강우량 자료를 예측하고 확률강우량을 산정함으로써 시계열분석을 통한 확률강우량 산정과 경향성을 고려하여 산정된 확률강우량을 비교했다. 우선 실제로 우리나라의 강우의 패턴이 변화하고 있는지 확인하고, 변화의 양상이 뚜렷한 지점에 대해서 시계열분석을 이용하여 가까운 미래의 확률강우량을 산정하였다. 그 결과, 2010년에 비해서 2020년의 확률강우량이 4~15%정도 증가하였다. 다른 방법과 비교해본 결과, 약 5%의 편차를 보였다. 본 연구에서는 최종적으로 우리나라 강우관측소 61지점의 경향성을 판별하여 전국 지도에 등고선으로 나타내어 경향성을 고려해야 할 지역들은 분류하였고, 이 지도를 활용하여 확률강우량을 산정함으로써 수공구조물의 계획 및 설계, 하천관리, 수자원 계획 등에 활용하고 전체적인 설계 빈도 상향조정으로 발생되는 예산 낭비 방지와 홍수피해 저감에 도움이 되고자 한다.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.597-607
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• 2011

Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1647-1652
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.397-404
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• 1999

1997년말 발생한 외환위기 이후 불확실성의 증대로 시계열모형을 이용한 경제예측에 한계가 노정되고 있다. 이를 극복하기 위하여 경제주체의 기대(expectation)를 파악할수 있는 기업경기실사지수를 경제예측에 도입할 필요가 있다. 본고에서는 기업경기실사지수를 이용한 모형과 시계열모형을 추정하고 이들을 예측력 측면에서 비교, 분석해보았다. 분석결과 불확실성이 높았던 외환위기이후 기간에는 기업경기실사지수를 이용한 모형이 시계열모형보다 예측력면에서 우수한 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.317-333
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• 1998

In this study, I employed the autoregressive model and the geometric Brownian motion model to analyze the recent stock prices of Korea. For all 7 series of stock prices(or index) the geometric Brownian motion model gives better predicted values compared with the autoregressive model when we use smaller number of observations.