• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.519-526
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• 2016

As marine accidents happen frequently, it is required to establish a marine traffic monitoring system, which is designed to improve the safety and efficiency of navigation in VTS (Vessel Traffic Service). For this aim, recently, X-band marine radar is used for extracting the sea surface information and, it is necessary to retrieve wave information correctly and provide for the safe and efficient movement of vessel traffic within the VTS area. In this paper, three different current estimation algorithms including the classical least-squares (LS) fitting, a modified iterative least-square fitting routine and a normalized scalar product of variable current velocities are compared with buoy data and then, the iterative least-square method is modified to estimate wave information by improving the initial current velocity. Through several simulations with radar signals, it is shown that the proposed method is effective in retrieving the wave information compared to the conventional methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.128-141
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• 2018

In this paper, a methodology to analyze the potential safe benefit of six cooperative driver assistance systems via V2V (vehicle-to-vehicle) communications is proposed. Although it is quite necessary to assess social impact with respect to new safety technologies for cooperative vehicles with V2V communications, there are few studies in Korea to predict the quantitative safety benefit analysis. In this study, traffic accident scenarios are classified based on traffic fatality between passenger cars. The sequential collision type is classified for a multiple pile-up with respect to collision direction such as forward, side, head-on collisions. Then movement of surrounding vehicle is considered for the scenario classification. Next, the cooperative driver assistance systems such as forward collision warning, blind spot detection, and intersection movement assistance are related with the corresponding accident scenarios. Finally, it is summarized how much traffic fatality may be reduced potentially due to the V2V communication based safety services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.101-110
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• 2011

Recently, an attack to an application incapacitates the intrusion detection rule, the defense policy for a network and database and induces intrusion incidents. Thus, it is necessary to study integration security to ensure the security of an internal network and database from that attack. This article is about building an integration security system to prevent an attack to an application set with intrusion detection rules. It responds to network-based attack through detection, disperses attack with the internal integration security system through virtual clustering and load balancing, and sets up defense policy for attacking destination packets, analyzes and records attack packets, and updates rules through monitoring and analysis. Moreover, this study establishes defense policy according to attacking types to settle access traffic through virtual machine partition policy and suggests an integration security system applied to prevent attack and tests its defense. The result of this study is expected to provide practical data for integration security defense for hacking attack from outside.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.5
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• pp.471-478
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• 2013

In this paper, we have designed and implemented an obstacle detecting device based laser radar. It is an alternative to solve through problem analysis of that are currently operated safety equipment and status research of domestic railway crossing. It is target to improve the safety and reliability of the rail traffic through effective obstacle detection at crossing account for a large proportion of train accidents. suggest a system to overcome the problems caused by aging and limitation of existing safety equipment. Design a crossing obstacle detection device that utilizes laser radar scanner, proved this through performance evaluation and testing of the prototype.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.1-7
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• 2019

Recently, various approaches to detect network attacks using machine learning have been studied and are being applied to detect new attacks and to increase precision. However, the machine learning method is dependent on feature extraction and takes a long time and complexity. It also has limitation of performace due to learning data imbalance. In this study, we propose a method to solve the degradation of classification performance due to imbalance of learning data among the limit points of detection system. To do this, we generate data using Generative Adversarial Networks (GANs) and propose a classification method using Convolutional Neural Networks (CNNs). Through this approach, we can confirm that the accuracy is improved when applied to the NSL-KDD and UNSW-NB15 datasets.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.173-183
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• 2009

We present a real-time monitoring system for detecting anomalous network events using the entropy. The entropy accounts for the effects of disorder in the system. When an abnormal factor arises to agitate the current system the entropy must show an abrupt change. In this paper we deliberately model the Internet to measure the entropy. Packets flowing between these two networks may incur to sustain the current value. In the proposed system we keep track of the value of entropy in time to pinpoint the sudden changes in the value. The time-series data of entropy are transformed into the two-dimensional domains to help visually inspect the activities on the network. We examine the system using network traffic traces containing notorious worms and DoS attacks on the testbed. Furthermore, we compare our proposed system of time series forecasting method, such as EWMA, holt-winters, and PCA in terms of sensitive. The result suggests that our approach be able to detect anomalies with the fairly high accuracy. Our contributions are two folds: (1) highly sensitive detection of anomalies and (2) visualization of network activities to alert anomalies.

• Journal of KIISE
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• v.44 no.1
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• pp.1-12
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• 2017

The continuously growing internet service requirements has resulted in a multitier system structure consisting of web server and database (DB) server. In this multitier structure, the existing intrusion detection system (IDS) detects known attacks by matching misused traffic patterns or signatures. However, malicious change to the contents at DB server through hypertext transfer protocol (HTTP) requests at the DB server cannot be detected by the IDS at the DB server's end, since the DB server processes structured query language (SQL) without knowing the associated HTTP, while the web server cannot identify the response associated with the attacker's SQL query. To detect these types of attacks, the malicious user is tracked using knowledge on interaction between HTTP request and SQL query. However, this is a practical challenge because system's source code analysis and its application logic needs to be understood completely. In this study, we proposed a scheme to find the HTTP request associated with a given SQL query using only system log files. We first generated an HTTP request-SQL query map from system log files alone. Subsequently, the HTTP request associated with a given SQL query was identified among a set of HTTP requests using this map. Computer simulations indicated that the proposed scheme finds the HTTP request associated with a given SQL query with 94% accuracy.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.519-534
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• 2008

The Maglev travels at levitated status from the rail in some gab (about $8\sim10mm$). it make difference with the existing subway or the another LRV. The detection method of the train speed and the train position to be used at Maglev's signaling system differ with the existing subway or the another LRV's. so, the signal system construction of the new method is necessary. This paper describes the configuration and characteristic of the total signaling system (TTC/Wayside/Cab/Guide way system etc.) developed for Maglev, and the design concept of the signaling system based on the latest wireless LAN communication for driverless operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.948-956
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• 2014

Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.