• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.87-88
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• 2015

최근 IT기술의 발전과 더불어 보안의 중요성이 부각되면서 Pin Number, Password, Pattern Recognition 등 인증 방식에 대한 연구가 진행되고 있지만 위와 같은 One-factor 인증 시스템에는 "Shoulder Attack"과 같은 사용자 레벨에서의 보안공격에 취약하다. 위와 같은 문제점을 해결하기 위하여 'Google E-mail' 등 일부 강화된 보안이 필요한 시스템에서는 추가 모듈을 이용한 Two-factor 인증 시스템을 적용하여 보안을 제공하고 있지만 사용상의 번거로움과 복잡성으로 인해 고도의 보안 기술의 적용을 받지 못하는 등 많은 제약사항이 남아있다. 본 논문에서는 위 와 같은 One-factor 시스템의 취약점을 파악하여 그에 따라 보안 인증 절차를 향상시키기 위해 암호화와 인증 방법으로 지문인식을 사용하여 Multi-level 인증 시스템을 제안한다. 본 시스템은 Send 디비이스를 구현한 아두이노를 통해 M2M 서비스를 수행하며, 암호와 지문 정보를 아두이노 디바이스에 저장하여 두 가지의 신뢰적인 정보를 바탕으로 인증하는 시스템이다. 아두이노를 이용하여 디바이스 분리를 통한 사용자 레벨에서의 보안을 강하고 지문인식을 통해 불편함과 복잡성을 간소화하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1688-1696
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• 2020

The paradigm of Internet-of-things(IoT) systems is changing from a cloud-based system to an edge-based system to solve delays caused by network congestion, server overload and security issues due to data transmission. However, edge-based IoT systems have fatal weaknesses such as lack of performance and flexibility due to various limitations. To improve performance, application-specific hardware can be implemented in the edge device, but performance cannot be improved except for specific applications due to a fixed function. This paper introduces a edge-centric metamorphic IoT(mIoT) platform that can use a variety of hardware through on-demand partial reconfiguration despite the limited hardware resources of the edge device, so we can increase the performance and flexibility of the edge device. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to previous studies in which the reconfiguration algorithm was executed on the server.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.787-799
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• 2023

As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1992.11a
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• pp.127-130
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• 1992

GF(2m) 이론은 switching 이론과 컴퓨터 연산, 오류 정정 부호(error correcting codes), 암호학(cryptography) 등에 대한 폭넓은 응용 때문에 주목을 받아 왔다. 특히 유한체에서의 이산 대수(discrete logarithm)는 one-way 함수의 대표적인 예로서 Massey-Omura Scheme을 비롯한 여러 암호에서 사용하고 있다. 이러한 암호 system에서는 암호화 시간을 동일하게 두면 고속 연산은 유한체의 크기를 크게 할 수 있어 비도(crypto-degree)를 향상시킨다. 따라서 고속 연산의 필요성이 요구된다. 1981년 Massey와 Omura가 정규기저(normal basis)를 이용한 고속 연산 방법을 제시한 이래 Wang, Troung 둥 여러 사람이 이 방법의 구현(implementation) 및 곱셈기(Multiplier)의 설계에 힘써왔다. 1988년 Itoh와 Tsujii는 국제 정보 학회에서 유한체의 역원을 구하는 획기적인 방법을 제시했다. 1987년에 H, W. Lenstra와 Schoof는 유한체의 임의의 확대체는 원시정규기저(primitive normal basis)를 갖는다는 것을 증명하였다. 1991년 Stepanov와 Shparlinskiy는 유한체에서의 원시원소(primitive element), 정규기저를 찾는 고속 연산 알고리즘을 개발하였다. 이 논문에서는 원시 정규기저를 찾는 Algorithm을 구현(Implementation)하고 이것이 응용되는 문제들에 관해서 연구했다.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.151-164
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• 2009

The importance of sensor networks as a base of ubiquitous computing realization is being highlighted, and espicially the security is recognized as an important research isuue, because of their characteristics.Several efforts are underway to provide security services in sensor networks, but most of them are preventive approaches based on cryptography. However, sensor nodes are extremely vulnerable to capture or key compromise. To ensure the security of the network, it is critical to develop security Intrusion Detection System (IDS) that can survive malicious attacks from "insiders" who have access to keying materials or the full control of some nodes, taking their charateristics into consideration. In this perper, we design a distributed and adaptive IDS architecture on sensor networks, respecting both of energy efficiency and IDS efficiency. Utilizing a modified HEED algorithm, a clustering algorithm, distributed IDS nodes (dIDS) are selected according to node's residual energy and degree. Then the monitoring results of dIDSswith detection codes are transferred to dIDSs in next round, in order to perform consecutive and integrated IDS process and urgent report are sent through high priority messages. With the simulation we show that the superiorities of our architecture in the the efficiency, overhead, and detection capability view, in comparison with a recent existent research, adaptive IDS.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.739-746
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• 2016

A total of 152 pig farms were randomly selected from the five provinces in South Korea. During the experiment, the average temperature and relative humidity was $24.7^{\circ}C$ and 74% in summer and $2.4^{\circ}C$ and 53% in winter, respectively. The correlation between floor space allowance (FSA) and productivity index was analyzed, including non-productive sow days (NPD), number of weaners (NOW), survival rate (SR), appearance rate of A-grade pork (ARA), and days at a slaughter weight of 110 kg (d-SW) at different growth stages. The objectives of the present study were i) to determine the effect of FSA on the pig productivity index and ii) to suggest the minimum FSA for pigs based on scientific baseline data. For the pregnant sow, NPD could be decreased if pregnant sows were raised with a medium level (M) of FSA (3.10 to $3.67m^2/head$) while also keeping the pig house clean which improves hygiene, and operating the ventilation system properly. For the farrowing sows, the NOW tended to decrease as the FSA increased. Similarly, a high level of FSA (H) is significantly negative with weaner SR of farrowing sows (p-value = 0.017), indicating this FSA tends to depress SR. Therefore, a FSA of 2.30 to $6.40m^2/head$ (very low) could be appropriate for weaners because a limited space can provide a sense of security and protection from external interruptions. The opposite trend was observed that an increase in floor space (> $1.12m^2/head$ leads to increase the SR of growing pigs. For the fattening pigs, H level of FSA was negatively correlated with SR, but M level of FSA was positively correlated with SR, indicating that SR tended to increase with the FSA of 1.10 to $1.27m^2/head$. In contrast, ARA of male fattening pigs showed opposite results. H level of FSA (1.27 to $1.47m^2/head$) was suggested to increase productivity because ARA was most affected by H level of space allowance with positive correlation ($R^2=0.523$). The relationship between the FSA and d-SW of fattening pigs was hard to identify because of the low $R^2$ value. However, the farms that provided a relatively large floor space (1.27 to $1.54m^2/head$) during the winter period showed d-SW was significantly and negatively affected by FSA.

• Annual Conference of KIPS
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• 2015.10a
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• pp.878-879
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• 2015

어플리케이션 구동 시에 화면에 표시되는 데이터는 보안 알고리즘이 적용되지 않은 채로 사용자 및 악의적인 해커들에게 노출된다. 본 논문에서는 악의적인 사용자가 화면을 캡쳐하여 이미지 파일로 저장하거나 디지털카메라로 화면에 노출된 정보의 사진을 찍었을 때 중요 데이터가 유출되지 않도록 화면 출력 이미지를 디지털 영상 처리 기법을 이용하여 변조한다. 사용자가 육안으로 볼 때에는 데이터를 식별할 수 있도록 화면 주사율에 맞추어 변조된 영상에 대한 보완 영상을 번걸아 출력한다.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.199-208
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• 2022

The world health organization classified the emerging coronavirus (known as Covid-19) as a pandemic after confirming the extent of spread and scale. As a matter of fact, outbreaks of similar scale or even worse have been witnessed throughout history. Thus, the development of prevention strategies exists to protect against such calamaties. One of the widely proven measures that controls the spread of any contagious diseases is social distancing. As a result, this paper will demonstrate the concept of an application "Be Aware" on enabling the implementation of this preventive measure. In particular "Be aware" evaluates the extent of congestion in public places using current time data. The proposed project will use Global Positioning System (GPS), and Application Programming Interface (API), to ensure information accuracy, and the API use Crowdsourcing to collect Real-Time Data (RTD) from the selected places. One line

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.