• Journal of KIISE
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• v.44 no.6
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• pp.595-600
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• 2017

Android applications are composed of one or more components. The components within an application or several applications may interact with each other primarily through intents. Such interactions may cause security and reliability issues such as broadcast theft, activity hijacking, and intent spoofing. These problems need to be resolved through testing techniques using various interaction test scenarios before an application gets launched. However, with the existing test scenario generation approach, some infeasible test scenarios may be generated since they do not consider the re-execution order based on activity setting when pressing the back button. This paper proposes a test case generation technique which removes infeasible interaction paths by utilizing the activity stack information.

• Convergence Security Journal
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• v.15 no.3_2
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• pp.71-76
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• 2015

Global companies are building global production network for the demand of the global market. However, the establishment and operation of overseas plants make the unexpected case like theft, low will to produce and salary issue of local workers. Thus in this paper, we propose the raw materials loss prevention model based on IP Camera and RFID Box for raw materials management of overseas plants. This model can prevent the theft or loss of raw materials write inventory up in real-time. This model can be allows us to realize the cost-effective production management because it enables remote inventory management. And this model can prevent brand image danage and profit loss due to reject product.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.16-26
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• 2016

This proposed project is about a bicycle anti theft devised system which helps people protect the bicycle from theft and helps to track the stolen bicycle's location using a smart phone. Safety bike uses two main devices to keep the bicycle secured, the vibration sensor and GPS sensor. The purpose of this project is to put all these small devices into one well connected system which will help the bicycle owner have more control over the security of his own bicycle. The whole system can be divided into two main parts. The first part is about the hardware development whereby all electronics components are connected via the circuit design using wire wrapping technique. This hardware part includes, a vibrations sensor, a GPS receiver, a toggle switch, LED light, Bluetooth and a buzzer. Wireless Bluetooth signals are used as the means of communication between the smartphone and the microcontroller. The second part is the software part which is being to program and control the whole system. The program is written using MikroBasic, a full-featured Basic compiler for microcontroller based systems. In conclusion, this system is designed to enable user to have control in securing his/her bicycle also being able to find and locate it at any time using GPS receiver and mobile android application.

• Journal of the Korean housing association
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• v.19 no.2
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• pp.73-82
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• 2008

The purpose of this paper is to analyze the correlation analysis between the experienced crime victimization rate and the evaluation indicator of residents' safety of outdoor spaces from crime in multi-family housing. Additionally this paper intend to analyze the correlation analysis between the residents' satisfaction about safety and the evaluation indicator of residents' safety of outdoor spaces. For that purpose, 9 Multi-Family Housing located in the metropolitan area were selected to perform a survey against 349 residents during May 26-29, 2006. The SPSS program was used and the level of satisfaction ranged from 1 to 5. Scale of 5 being most satisfied while 1 being most dissatisfied. The results of this study are the followings; 1) Most occurred crime were auto theft and damages, poster matter theft, housebreaking. 2) The crime rate is higher in outdoor than in indoor. 3) This study proved the correlation between the residents' satisfaction about housing safety and most indicators except the number of households etc. 4) By the results of the correlation coefficient it makes clear that the number of apartment building, the visibility of Green Space, the location of pedestrian etc. have relation with the residents' satisfaction about housing safety.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.101-103
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• 2013

Most pickpockets occurs at a place where a lot of people. However, the current occurs more commonly in a secluded place and unfrequented place. In this paper, we classified to the scene for submitting to image forensics evidence target for pickpockets of theft. Using the ${\chi}^2$ histogram to detect the scene change detection. We wish to submit evidence by classifying as a pickpocket scene video.

• Human Ecology Research
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• v.53 no.6
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• pp.641-654
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• 2015

This study identifies and tracks changes gender differences in adolescent users' problematic online behavior. This study used Korea Youth Panel Survey (KYPS), which has tracked respondents over 7 years, with self-control theory and social learning theory applied as a theoretical framework. The model included individual-level variables such as self-control and respondent's experience of problematic behavior (offline), as well as socialization variables such as the number close friends who engaged in problematic offline behavior, parent-child relationships, and parental monitoring. Dependent variables included problematic online behavior, unauthorized ID use (ID theft) and cyberbullying (cursing/insulting someone in a chat room or on a bulletin board). Control variables consisted of academic performance, time spent on a computer, monthly household income, and father's educational attainment. Random and fixed effects models were performed by gender. Results supported self-control theory even for the within-level analysis (fixed effects models) regardless of gender, while social learning theory was partially supported. Only peer effects were found significant (except for unauthorized ID use) among girls. Year dummy variables showed significant negative associations; however, academic performance and time spent using computers were significant in some models. Father's educational attainment and monthly household income were found insignificant, even in the random effects models. We also discuss implications and suggestions for future research and policy makers.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.4
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• pp.177-180
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• 2013

A software birthmark means inherent characteristics that can be used to identify a program. Because the software birthmark is difficult to remove by simple program transformation, it can be used to detect code theft. In this paper, we propose a birthmark technique based on API k-gram of Android applications. Android SDK provides various libraries that help programmers to develop application easily. In order to use Android SDK, we have to use API method calls. The API call instructions are hard to be replaced or removed, so they can be a inherent characteristics of an application. To show the effectiveness of the proposed birthmark, we compared it with previous birthmarks and evaluated it with open source applications. From the experiments, we verified that the credibility and resilience of our birthmark is higher than previous birthmarks.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.71-76
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• 2020

Recently, the market of private study room continues to grow due to increased usage of private study room. However, old study room operators often have problems with cleanliness and theft, and cannot handle them in real time. Also, there is a problem of wasting power since users usually leave their desk for a long time with the lights on. Thus, in this paper, we intended to design a service which can prevent theft, clean up the user's desk, and adjust lighting system according to the user's condition based on Arduino system. Also, we tried to design a system which sends notifications from information of android-based IoT(Internet of Things), to the terminal when other people approach to the user's desk or try to steal user's item.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.