• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.5
• /
• pp.1020-1025
• /
• 2007

EMS(energy management system) and SCADA(supervisory control and data acquisition) systems are used for reliable and efficient operation of electrical power systems. Various functions in EMS such as power flow, contingency analysis, security analysis essentially need accurate data set for reliable operation. State estimation can be a tool for providing these data. In this paper, programs for observability analysis and bad data processing are developed. Fundamental algorithms are introduced and validity of the proposed techniques is inspected with test cases.

• Journal of Information Processing Systems
• /
• v.1 no.1 s.1
• /
• pp.9-13
• /
• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.8
• /
• pp.3687-3691
• /
• 2012

Various digital contents generation techniques are widely utilized by upgrading PC and mobile device. In this paper, we propose sketch effect simulation based on real drawing pieces. For this, we analyze Vincent Van Gogh's drawing pieces; and then construct DB by extracting sketch stroke pattern of each object. From this database, we select stroke pattern at each object, and then, apply it. Our algorithm can generate similar effect look like real drawing piece. It may be utilized various contents such as children's painting education book.

• Journal of Multimedia Information System
• /
• v.5 no.2
• /
• pp.99-104
• /
• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Information Processing Systems
• /
• v.13 no.6
• /
• pp.1585-1601
• /
• 2017

With the rapid development of computer technologies, a number of image modification methods have emerged, which have great impacts on the security of image information. Therefore, it is necessary to protect the integrity and authenticity of digital images, and digital watermarking technique consequently becomes a research hotspot. An effort is made to survey and analyze advancements of image watermarking algorithms based on singular value decomposition (SVD) in recent years. In the first part, an overview of watermarking techniques is presented and then mathematical theory of SVD is given. Besides, SVD watermarking model, features, and evaluation indexes are demonstrated. Various SVD-based watermarking algorithms, as well as hybrid watermarking algorithms based on SVD and other transforms for copyright protection, tamper detection, location, and recovery are reviewed in the last part.

• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.882-895
• /
• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• Smart Media Journal
• /
• v.6 no.2
• /
• pp.15-25
• /
• 2017

The security and privacy issues derived from unsecurely storing biometrics templates in biometric authentication/ recognition systems have opened a new research area about how to secure the stored biometric templates. Biometrics-based key generation is the newest approach that provides not only a mechanism to protect stored biometric templates in authentication/ recognition systems, but also a method to integrate biometric systems with cryptosystems. Therefore, this approach has attracted much attention from researchers worldwide. A review of current research state to summarize the achievements and remaining works is necessary for further works. In this study, we first outlined the requirements and the primary challenges when implementing these systems. We then summarize the proposed techniques and achievements in representative studies on biometrics-based key generation. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further researches in this area.

• Annual Conference of KIPS
• /
• 2015.04a
• /
• pp.508-510
• /
• 2015

2014년을 기준으로 안드로이드 OS기반 태블릿이 전체 태블릿 시장의 67.4%를 차지하고 있고, 스마트폰은 약 80%에 육박하는 시장 점유율을 가지고 있으므로 스마트폰 사용자 5명 중 4명은 안드로이드 스마트폰을 사용한다. 스마트폰이 가진 편리성으로 인해 급속도로 확산되고 있는 스마트폰 중 특허 안드로이드 환경의 스마트폰의 보안 취약점을 이용한 보안사고가 꾸준히 증가하고 있다. 스마트폰에는 주소록, SMS, 위치 정보 등의 많은 개인정보들이 담겨 있는데, 스마트폰이 가지고 있는 다양한 종류의 보안 취약점을 이용하여 개인 정보를 갈취하고 악용하는 등의 악의적인 목적의 공격들이 끊임없이 발생하고 있다. 따라서 본 논문은 개인정보의 유출을 막기 위한 다양한 보안 기법에 대해 살펴보고자 한다.

• Structural Engineering and Mechanics
• /
• v.22 no.1
• /
• pp.17-32
• /
• 2006

The application of cellular materials in load-carrying and security-relevant structures requires the exact prediction of their mechanical behavior, which necessitates the development of robust simulation models and techniques based on appropriate experimental procedures. The determination of the yield surface requires experiments under multi-axial stress states because the yield behavior is sensitive to the hydrostatic stress and simple uniaxial tests aim only to determine one single point of the yield surface. Therefore, an experimental technique based on a uniaxial strain test for the description of the influence of the hydrostatic stress on the yield condition in the elastic-plastic transition zone at small strains is proposed and numerically investigated. Furthermore, this experimental technique enables the determination of a second elastic constant, e.g., Poisson's ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2180-2197
• /
• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.