• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.1
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• pp.81-87
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• 2020

Recently, the network configuration is being rapidly changed to enable easy and free network service configuration based on SDN/NFV. Despite the many advantages and applications of SDN, many security issues such as Distributed Denial of Service (DDoS) attacks are being constantly raised as research issues. In particular, the effectiveness of DDoS attacks is much faster, SDN is causing more and more fatal damage. In this paper, we propose an entropy-based technique to detect and mitigate DDoS attacks in SDN, and prove it through experiments. The proposed scheme is designed to mitigate these attacks by detecting DDoS attacks on single and multiple victim systems and using time - specific techniques. We confirmed the effectiveness of the proposed scheme to reduce packet loss rate by 20(19.86)% while generating 3.21% network congestion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.15-18
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• 2023

UAV는 군사용을 처음 시작으로 근래에 취미용 드론의 급격한 성장과 더불어 최근 기후변화, 교통혼잡, 범죄 예방 등 여러 사회 문제 해결을 위한 드론의 필요성이 증가함에 따라 건설, 교통, 농업, 에너지, 엔터테인먼트 등 다양한 산업과 여러 사회 서비스로 그 필요성이 확대되고 있다. 본 연구는 이러한 사회적 흐름에 따라 인공지능 기술을 통한 드론의 활용성을 확대하고 GPS 수신이 안 되는 환경에서 딥러닝 객체 탐지 모델을 활용한 자율 착륙을 연구를 목표로 한다. GPS 신호는 실내와 같은 환경 혹은 지하, 교량 아래, 산속 등과 같은 곳에서는 수신이 어렵다. 이를 극복하고자 GPS 신호수신이 어려운 지역에서 GPS 수신기를 통해 받는 위치 정보 대신 드론에 장착된 카메라를 통해 전달받는 영상에서 착륙할 지점을 인식하고 카메라를 통해 받는 영상 정보만 이용하여 목표지점으로 하강하는 방식으로 자율 착륙을 유도한다. 딥러닝 중 경량화 모델을 활용하여 소형 드론에서 실시간으로 착륙 지점을 감지하기 위해 최적화 과정을 진행해 실시간 자율 착륙이 가능하게 하였다. 본 연구를 통해 드론의 착륙에 있어 GPS 수신기와 사람의 조종에 대한 의존도를 낮출 수 있을 것으로 기대한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.12-21
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• 2012

IEEE 802.11 MAC protocol standard, DCF(CSMA/CA), is originally designed to ensure the fair channel access between mobile nodes sharing the local wireless channel. It has been, however, revealed that some misbehavior nodes transmit more data than other nodes through artificial means in hot spot area spreaded rapidly. The misbehavior nodes may modify the internal process of their MAC protocol or interrupt the MAC procedure of normal nodes to achieve more data transmission. This problem has been referred to as a selfish node problem and almost literatures has proposed methods of analyzing the MAC procedures of all mobile nodes to detect the selfish nodes. However, these kinds of protocol analysis methods is not effective at detecting all kinds of selfish nodes enough. This paper address this problem of detecting selfish node using Tsallis-Entropy which is a kind of statistical method. Tsallis-Entropy is a criteria which can show how much is the density or deviation of a probability distribution. The proposed algorithm which operates at a AP node of wireless LAN extracts the probability distribution of data interval time for each node, then compares the one with a threshold value to detect the selfish nodes. To evaluate the performance of proposed algorithm, simulation experiments are performed in various wireless LAN environments (congestion level, how selfish node behaviors, threshold level) using ns2. The simulation results show that the proposed algorithm achieves higher successful detection rate.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3176-3183
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• 2011

Traffic and congestion control in the wireless sensor network is an important parameter that decides the throughput and QoS (Quality of Service). This paper proposes a transmission rate priority-based traffic control scheme to serve digital contents streaming in wireless sensor networks. In this paper, priority for transmission rate decides on the real-time traffic and non-real-time with burst time and length. This transmission rate-based priority creates low latency and high reliability so that traffic can be efficiently controlled when needed. Traffic control in this paper performs the service differentiation via traffic detection process, traffic notification process and traffic adjustment. The simulation results show that the proposed scheme achieves improved performance in delay rate, packet loss rate and throughput compared with those of other existing CCF and WCA.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.527-532
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• 2020

Seoul terminal maneuvering area (TMA) that includes Incheon International Airport (ICN) and Gimpo International Airport is a very congested airspace with around 1,000 daily flights and the airspace blocked at the boundary between Incheon flight information region (FIR) and Pyongyang FIR. Consequently, with frequency radar vectorings, separation assurance in this airspace is complicated thus resulting in higher controller workload. In this paper, the conflict and collision risks in Seoul TMA are analyzed using recorded ADS-B data for past three years. Using the recorded trajectories, original flight plan procesures and routes are reconstructed and the risks are quantified using detect and avoid well clear (DWC) metric that is developed for large unmanned aircraft system. The region west of ICN was found to be the highest risk area regardless of the runway directions. In addition, merge and crossing points between procedures displayed relatively high risks.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.197-205
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• 2021

Recently, the retail industry has been increasingly demanding information technology convergence and utilization to respond to various external environmental threats such as COVID-19 and to be competitive using AI technologies, but there is a very lack of research and application services. This study is a CCTV video data-driven AI application case study, using CCTV image data collection in retail space, object detection and tracking AI model, time series database to store real-time tracked objects and tracking data, heatmap to analyze congestion and interest in retail space, social access zone.We present the orientation and verify its usability in the direction designed through practical implementation.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.