• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.6
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• pp.147-154
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• 2000

A numerical procedure to analyze 3-dimensional elastohydrodynamic lubrication was applied on the cam-roller contact of the valve mechanism for a marine diesel engine. Both the pressure distribution and the film thickness between the cam and roller follower were calculated for each time step of the whole cycle. The pressure spike is shown at the outlet of the roller edge and it is getting higher as the external load is increased. An effective profiling method for the roller edge was suggested using the results of elastohydrodynamic lubrication analysis and the peak pressure was removed completely with the new profiling.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.39-49
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• 2020

In this paper, we show that the Chinese standard block cipher SM4 is vulnerable to the side channel attacks and present a countermeasure to resist them. We firstly validate that the secret key of SM4 can be recovered by differential power analysis(DPA) and correlation power analysis(CPA) attacks. Therefore we analyze the vulnerable element caused by power attack and propose a first order masking-based countermeasure to defeat DPA and CPA attacks. Although the proposed countermeasure unfortunately is still vulnerable to the profiling power attacks such as deep learning-based multi layer perceptron(MLP), it can sufficiently overcome the non-profiling attacks such as DPA and CPA.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.15-23
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• 2017

Template attack is a powerful side-channel analysis technique which can be performed by an attacker who has a test device that is identical to target device. Template attack is consisted of building template in profiling phase and matching the target device using template that were calculated in profiling phase. One methods to improve the success rate of template attack is to better estimate template which is consisted sample mean and sample covariance matrix of gaussian distribution in template profiling. However restriction of power trace in profiling phase led to poor template estimation. In this paper, we propose new method to select noisy power trace in profiling phase. By eliminating noisy power trace in profiling phase, we can construct more advanced mean and covariance matrix which relates to better performance in template attack. We proved that the proposed method is valid through experiments.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.686-694
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• 2014

Criminal profiling, also known as offender profiling is designed to predict the characteristics of unknown criminal perpetrator through an analysis of the crime scene. Until now, there has been conflict about the effectiveness of criminal profiling among academics. In this study, 113 police investigators', working in serious crime divisions, were interviewed about their experiences with criminal profiling, and their belief about its effectiveness. 63.7% of the respondents agreed that criminal profiling is a valuable investigative tool and 62.8% agreed that profilers are valuable to criminal investigations. A total of 31.8% agreed that profilers help the police identify offenders and 15.0% agreed that there is no risk of profiler misdirecting and investigation. 61.5% of the respondents who had reported using a profile agreed that profiling is helpful and 71.4% told that they would use profiling again in the future.

• Korean Security Journal
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• no.48
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• pp.113-146
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• 2016

Criminal profiling is a effective and efficient measure for enhancing industrial security of casino business. Particularly, developing criminal profiling of deviant behaviors in casino will help security management to become more effective and efficient in practical ways. Unfortunately, however, there is lack of empirical profiling study in this regard. To fill the vacuum of literature on this topic, this study was purported to create offender profiles of different types of deviant behaviors in casino based on various theories and techniques in criminal profiling literature, such as investigative psychology, linkage analysis, and behavioral evidence analysis. To fulfill the purposes, this study collected behavioral evidence from 90 casino security officers in South Korea. Offenders' behavioral evidence was analyzed to develop offender profiles of seven different types of deviant behaviors, and then the profiles were compared with each profiles that security officers focus on to identify offenders during their work hours. Results showed that, first, there were unique profiles of each type of seven different categories of deviant behaviors in terms of offenders' ways of speaking and acting, their appearance and attitudes. In addition, this study found that there were some amount of gaps between actual offenders' profiles and profiles that security officers have in mind. Based on the results, this study provided policy implications in terms of managing casino industrial security, education and training for security officers, and future study on casino security.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Bulletin of the Korean Chemical Society
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• v.34 no.5
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• pp.1467-1472
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• 2013

Metabolomics is a field that studies systematic dynamics and secretion of metabolites from cells to understand biological pathways based on metabolite changes. The metabolic profiling of intact human colorectal tissues was performed using high-resolution magic angle spinning (HR-MAS) NMR spectroscopy, which was unnecessary to extract metabolites from tissues. We used two different groups of samples, which were defined as normal and cancer, from 9 patients with colorectal cancer and investigated the samples in NMR experiments with a water suppression pulse sequence. We applied target profiling and multivariative statistical analysis to the analyzed 1D NMR spectra to identify the metabolites and discriminate between normal and cancer tissues. Cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, tyrosine relative to normal tissue. In the OPLS-DA (orthogonal partial least square discriminant analysis), the score plot showed good separation between the normal and cancer groups. These results suggest that metabolic profiling of colorectal cancer could provide new biomarkers.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2007.11a
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.263-270
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• 2016

In this study, it was attempted to develop a grade-customized statistical analysis system that can be operated by a teacher without professional knowledge of statistics by utilizing profiling in the education sector. For this, with the convergence of techniques of profiling into the education sector, it examined the elements necessary for building a customized student multidimensional analysis system. Referring to the overall configuration and the current state to build multidimensional analysis system utilizing practical profiling, it showed the implementation result of the algorithm applied to each statistical method, and presented the differences and superiority to existing systems. Once the system based on the proposed techniques is built, considering differences of students' needs and abilities and clarifying precise objectives and standards, with the improvement of satisfaction in public education, it is possible not only to reduce expense of prior and private learning but also realize self-directed learning suitable to one's learning ability and aptitude.