• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3399-3419
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• 2020

In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.

• International Journal of Computer Science & Network Security
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• 제21권12호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3523-3543
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• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• 전자공학회논문지A
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• 제33A권6호
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• pp.23-32
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• 1996

This paper proposes a new buffer management scheme to service delay-sensitive traffic and loss-sensitive traffic fairly in ATM networks. The proposed scheme uses tow buffers for delay-sensitive traffic and loss-sensitive traffic. To satisfy the average delay time of delay-sensitive traffic, cells in real-time buffer are served first. When congestion occurs in nonreal-time buffer, low loss priority cell in real-time buffer can be pushed out by high loss priority cell in nonreal-time buffer can be transferred to real-time buffer considering threshold value in real-time buffer. Using computer simulation, the existing methods and proposed scheme are compared and analyzed with respect to cell loss rate and average delay time. Simulation results show that the proposed scheme have superior performance to conventional schemes.

• 한국정밀공학회지
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• 제14권11호
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• pp.11-16
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• 1997

In the stroke sensitive shock absorber, the oil path is formed along the internal cylinder surface to make the oil flow during the piston's upper-lower reciprocation movement. In constrast with the conventional shock absorbesr which show one dynamic characteristic curve, stroke sensitive shock absorber shows two kinds of dynamic characteristics according to the stroke. In this study, in order to obtain more precise information about design and damping performance analysis, the analysis on the damping force generation process and dynamic behaviour characteristics of stroke sensitive shock absorber are performed by considering the valve characteristics.

• 한국광학회:학술대회논문집
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• 한국광학회 2003년도 제14회 정기총회 및 03년 동계학술발표회
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• pp.286-287
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• 2003

Polarization-sensitive optical coherence tomography (PS-OCT) was developed to image highly scattering tissues with accounting for polarization effects in the sample. These polarization-sensitive images can provide additional information on the structure of the tissue because of a polarization state of the light is changed at its interaction with biological tissues. The scattering and birefringence are two phenomena, which change the polarization state of light passing through medium. (omitted)

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.47-49
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• 2021

2020년 데이터 3법의 개정으로 의료데이터의 개인정보를 가명·익명 처리하여 통계작성, 연구, 공익적 기록 보존 등의 목적으로 사용가능 하도록 하고 있다. 그러나 비식별화 한 데이터를 유전정보, 신용정보 등을 이용하여 재식별이 가능하며, 재식별 정보를 통해 개인 건강정보는 민감정보로 프라이버시 침해에 악용될 여지가 있다. 본 논문에서는 민감정보로 분리되는 개인정보의 프라이버시 보호를 위한 동형암호의 필요성을 도출한다.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.427-433
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• 2000

In this paper, we review several two-stage randomized response techniques for gathering self-report data when persons are asked sensitive question. Also efficiencies and privacy protections based on the two-stage randomized response procedures are compared. Finally, we find optimal parameter conditions.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
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• pp.94-96
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• 2004

기술의 발달로 인하여 컴퓨터에 사용되는 메모리가 대용량화되고, 가격이 저렴해지면서 메인 메모리 데이터베이스 시스템이 주목을 받고 있다. 메인 메모리 데이터베이스 시스템은 디스크 기반 데이터베이스 시스템에 비해 디스크 접근을 줄임으로써, 좀 더 빠른 트랜잭션 처리를 보여주고 있다. 그러나 전원 차단과 같은 장애 발생 시, 메모리의 휘발성으로 인한 데이터 손실에 항상 대비를 해야 한다. 증권, 통신사와 같이 실시간 서비스가 이루어지고, 시스템 장애가 큰 손실로 이어지는 곳에서는 장애 발생 시 데이터의 빠른 복구를 필요로 하게 된다. 본 논문은 메인 메모리 데이터베이스 시스템에서 CST-트리(Cache Sensitive T-tree)보다 좋은 성능을 보이는 CST-트리(Cache Sensitive T-tree)에서 사용할 수 있는 인덱스의 빠른 구축 기법을 제안한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1251-1254
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• 2003

In this paper, Experience Sensitive Cumulative Neural Network (ESCNN) is introduced, which can cumulate the same or similar experiences. As the same or similar training patterns are cumulated in the network, the system recognize more important information in the training patterns. The functions of forgetting less important informations and attending more important informations resided in the training patterns are surveyed and implemented by simulations. The system behaves well under the noisy circumstances due to its forgetting and/or attending properties, even in 50 percents noisy environments. This paper also describes the creation of the generalized patterns for the input training patterns.