• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.847-857
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• 2019

Recently, various cyber threats such as Ransomware and APT attack are increasing by e-mail. The characteristic of such an attack is that it is important to take administrative measures by improving personal perception of security because it bypasses technological measures such as past pattern-based detection The purpose of this study is to investigate the human factors of employees who are vulnerable to spam mail attacks through field experiments and to establish future improvement plans. As a result of sending 7times spam mails to employees of a company and analyzing training report, It was confirmed that factors such as the number of training and the recipient 's gender, age, and workplace were related to the reading rate. Based on the results of this analysis, we suggest ways to improve the training and to improve the ability of each organization to carry out effective simulation training and improve the ability to respond to spam mail by awareness improvement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.632-640
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• 2013

In this paper, we proposed a system in a wireless LAN environment in case of security threats, the mobile terminal and the remote server-based WLAN security. The security of the wireless LAN environment in the recent technology in a variety of ways have been proposed and many products are being launched such as WIPS and DLP. However, these products are expensive and difficult to manage so very difficult to use in small businesses. Therefore, in this paper, we propose a security system, wireless LAN-based terminal and a remote server using whitelist according to development BYOD market and smartphone hardware. The proposed system that AP and personal device information to be stored on the server by an administrator and Application installed on a personal device alone, it has the advantage that can be Applicationlied to a variety of wireless network environment.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.91-96
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• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.397-407
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• 2017

Wireless networks make the users' work more convenient and efficient, but such networks can impair the availability of network resources and can cause leakage of important corporate information when there are security threats. In particular, damage has increased because of security attacks that take advantage of the vulnerabilities created by a wireless AP (Access Point). Public organizations and companies have gradually selected the WIPS (Wireless Intrusion Prevention System) to block wireless security threats and protect the internal network. However, it is very costly for other organizations and companies to introduce the WIPS solution. This paper proposes implementing a WIPS Sensor by using Raspberry Pi to reduce these costs and to block the various wireless LAN security threats. This implementation would protect corporate information and provide consistent services at a relatively reasonable price.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.856-858
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• 2013

The data which is sensed on USN(Ubiquitous Sensor Network) environment is concerned with personal privacy and the secret information of business, but it has more vulnerable characteristics, in contrast to common networks. In other words, USN has the vulnerabilities which is easily exposed to the attacks such as the eavesdropping of sensor information, the distribution of abnormal packets, the reuse of message, an forgery attack, and denial of service attacks. Therefore, the key is necessarily required for secure communication between sensor nodes. This paper proposes a KDPC(Key Distribution Protocol based on Cluster) using ECDH algorithm by considering the characteristics of sensor network. As a result, the KDPC can provide the safe USN environment by detecting the forgery data and preventing the exposure of sensing data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.666-668
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• 2013

Recently, the kind and number of malicious code, which operates in Operation System of smart devices, are rapidly increasing along with the fast supplement of smart devices. Especially, smart devices based on Android OS have high potential of danger to expose to malicious code as it has an easy access to system authority. When using intent, the global message system provided from Android, inter approach between applications is available, and possible to access to created data by the device. Intent provides convenience to application development in the aspect of reusability of component however, it could be appointed as a risk element in security-wise. Therefore, if intent is used in malicious purpose, it is easy to lead the condition where is weak on security. That is, it is possible to control as accessing to resources which application is carrying to operate by receiving intents as making smart device uncontrollable or consuming system resources. Especially, in case of system authority is achieved, the risks such as smart device control or personal information exposure become bigger when misusing broadcast intent through malicious code. This paper proposes a corresponding method of security vulnerability of Android intent that monitors the appearance of intent with intent pattern inspection, detects and blocks unidentified pattern intent.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.622-624
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• 2022

최근 편리성과 경제성 등의 이유로 개인형 이동장치인 전동 킥보드의 사용이 증가하고 있다. 사용자들은 앱으로 주변의 전동 킥보드 위치를 확인한 뒤, 가까운 기기를 찾아 이용한다. 하지만 전동 킥보드의 위치는 GPS로 표시되기 때문에 10 m 이상의 오차가 날 수 있다. 이를 보완하기 위해 (주)올룰로의 킥고잉은 사용자가 전동 킥보드 반납 시 촬영한 전동 킥보드 사진을 GPS 위치 정보와 함께 제공한다. 이 사진을 통해 다음 사용자는 더욱 정확히 전동 킥보드를 찾을 수 있다. 하지만 일부 사용자들은 전동 킥보드가 존재하지 않는 사진을 올리기도 하며, 따라서 사용자들이 촬영한 사진 중 실제 전동 킥보드가 존재하는 사진들만 제공하는 것은 매우 중요하다. 따라서 본 논문은 사용자들이 촬영한 사진 중 실제 전동 킥보드가 존재하는 사진들만 정확히 인식하는 YOLO 기반 시스템을 개발한다. 제안 방법은 (1) 전동 킥보드를 부분별로 탐지하는 기법과 (2) 전동 킥보드를 촬영된 각도에 따라 세분화하여 인식하는 기법을 사용한다. 실제 사용자들이 촬영한 사진을 사용한 실험 결과, 제안 방법은 기존 방법에 비해 더욱 정확히 전동 킥보드 사진을 인식하는 것을 확인하였다.

• Korean Journal of Cognitive Science
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• v.20 no.4
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• pp.535-554
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• 2009

Information collected on mobile devices might be utilized to support user's memory, but it is difficult to effectively retrieve them because of the enormous amount of information. In order to organize information as an episodic approach that mimics human memory for the effective search, it is required to detect important event like landmarks. For providing new services with users, in this paper, we propose the prediction model to find landmarks automatically from various context log information based on attributed Bayesian networks. The data are divided into daily and weekly ones, and are categorized into attributes according to the source, to learn the Bayesian networks for the improvement of landmark prediction. The experiments on the Nokia log data showed that the Bayesian method outperforms SVMs, and the proposed attributed Bayesian networks are superior to the Bayesian networks modelled daily and weekly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.705-708
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• 2018

Among biometrics commercialized for security, iris recognition technology has the most excellent security for the probability of the match between individuals is the lowest. Current commercialized iris recognition technology has excellent recognition ability, but this technology has a fatal drawback. Without the user's active cooperation, it cannot recognize the iris correctly. To make up for this weakness, recent trend of iris recognition development mounts a non-volunteering, unconstrained method. According to this information, the objective of this research is developing a module that can identify people iris from a video acquired by high performance infrared camera in a range of 3m and in a involuntary way. For this, we import images from the video and find people's face and eye positions from the images using Haar classifier trained through Cascade training method. finally, we crop the iris by Hough circle transform and compare it with data from the database to identify people.