• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.29-35
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• 2019

As the fourth industrial revolution is emerging, many companies are increasingly interested in smart factories and the importance of sensors is being emphasized. In the case that sensors for collecting sensing data fail, the plant could not be optimized and further it could not be operated properly, which may incur a financial loss. For this purpose, it is necessary to diagnose the status of sensors to prevent sensor' fault. In the paper, we propose a scheme to diagnose digital-sensor' fault by analyzing the rising time and falling time of digital sensors through the LSTM(Long Short Term Memory) of Deep Learning RNN algorithm. Experimental results of the proposed scheme are compared with those of rule-based fault diagnosis algorithm in terms of AUC(Area Under the Curve) of accuracy and ROC(Receiver Operating Characteristic) curve. Experimental results show that the proposed system has better and more stable performance than the rule-based fault diagnosis algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.63-68
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• 2019

Emerging applications such as Smart factory, in-car network, wide area power network require strict bounds on the end-to-end network delays. Flow-based scheduler in traditional Integrated Services (IntServ) architecture could be possible solution, yet its complexity prohibits practical implementation. Sub-optimal class-based scheduler cannot provide guaranteed delay since the burst increases rapidly as nodes are passed by. Therefore a leaky-bucket type regulator placed next to the scheduler is being considered widely. This paper proposes a simple server that achieves both fair scheduling and traffic regulation at the same time. The performance of the proposed server is investigated, and it is shown that a few msec delay bound can be achieved even in large scale networks.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.22-30
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• 2020

With the development of information and communication technology, DDoS and DRDoS continue to become security issues, and gradually develop into advanced techniques. Recently, IT companies have been threatened with DRDoS technology, which uses protocols from normal servers to exploit as reflective servers. Reflective traffic is traffic from normal servers, making it difficult to distinguish from security equipment and amplified to a maximum of Tbps in real-life cases. In this paper, after comparing and analyzing the DNS amplification and Memcached amplification used in DRDoS attacks, a countermeasure that can reduce the effectiveness of the attack is proposed. Protocols used as reflective traffic include TCP and UDP, and NTP, DNS, and Memcached. Comparing and analyzing DNS protocols and Memcached protocols with higher response sizes of reflective traffic among the protocols used as reflective traffic, Memcached protocols amplify ±21% more than DNS protocols. The countermeasure can reduce the effectiveness of an attack by using the Memcached Protocol's memory initialization command. In future studies, various security-prone servers can be shared over security networks to predict the fundamental blocking effect.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.605-610
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• 2020

Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.127-138
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• 2006

The IPSec is a basic security mechanism of the IPv6 protocol, which can guarantee an integrity and confidentiality of data that transmit between two corresponding hosts. Also, both data and communication subjects can be authenticated using the IPSec mechanism. However, it is difficult that the IPSec mechanism protects major important network from attacks which transmit mass abnormal IPSec traffic in session-configuration or communication phases. In this paper, we present a design of the security system that can effectively detect and defeat abnormal IPSec traffic, which is encrypted by the ESP extension header, using the IPSec Session and Configuration table without any decryption. This security system is closely based on a multi-tier attack mitigation mechanism which is based on network bandwidth management and aims to counteract DDoS attacks and DoS effects of worm activity.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1145-1150
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• 2020

After the Davos Forum, the recent 4th Industrial Revolution has become an area of interest to countries around the world. Among the technologies of the 4th industrial revolution, the ubiquitous computing environment requires a convergence environment of various devices, networks, and software technologies, and the RFID technology that identifies objects among the IoT technology fields is applied to all industries and has a competitive edge. Systems to which RFID technology is applied are being used in various industrial fields, especially! It is efficiently used for accurate inventory management and SCM management in the field of distribution and logistics. If the RFID system is built in a cloud-based environment, it will be possible to secure reliability in distribution management in consideration of an effective logistics management system and economic feasibility. This study is a study on the RFID system in a cloud computing environment to reduce the cost of operating or maintaining an application server to improve the economy and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4946-4960
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• 2020

In a device-to-device (D2D) underlaid cellular network, there exist two types of co-channel interference. One type is inter-layer interference caused by spectrum reuse between D2D transmitters and cellular users (CUEs). Another type is intra-layer interference caused by spectrum sharing among D2D pairs. To mitigate the inter-layer interference, we first derive the interference limited area (ILA) to protect the coverage probability of cellular users by modeling D2D users' location as a Poisson point process, where a D2D transmitter is allowed to reuse the spectrum of the CUE only if the D2D transmitter is outside the ILA of the CUE. To coordinate the intra-layer interference, the spectrum sharing criterion of D2D pairs is derived based on the (signal-to-interference ratio) SIR requirement of D2D communication. Based on this criterion, D2D pairs are allowed to share the spectrum when one D2D pair is far from another sufficiently. Furthermore, to maximize the energy efficiency of the system, a resource allocation scheme is proposed according to weighted graph coloring theory and the proposed ILA restriction. Simulation results show that our proposed scheme provides significant performance gains over the conventional scheme and the random allocation scheme.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.501-507
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• 2020

This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.