• The Korean Journal of Food And Nutrition
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• v.11 no.2
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• pp.165-170
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• 1998

Diagnostic radiology and conservative management for S75 patient with L1 lumbar fracture by traffic accidents were discussed with references, and then the obtained results were as follows ; 1. Wedging compression fractures with 10% deformity was confirmed at anterior vertebral body of L1 lumbar spine through lateral plain X-ray film. 2. Irregular bony fractures were observed at anterior vertebral body of L1 lumbar spine by CT scans, anatomically T12-L1 sites showed highly frequency of injuries, Denis's fracture type was classified as multiple compression fracture at anterior column without abnormal middle and posterior column, also no Cobb's angle, and then Frankel's neurological classification was E grade. 3. Orthopaedic treatments were performed with conservative methods. With rest on the bed, anti-in-flammatory medication, electrolyte and nutritional solution, the pain diminished. 4. After 3 weeks, rehabilitation was worked with putting on polyethylene back corset, although pains remained slightly until after 8 weeks, thereafter the spine showed gradually stability.

• 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 the Korea Convergence Society
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• v.10 no.5
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• pp.35-42
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• 2019

Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.35-45
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• 2004

In explosively increasing internet environments, information security is one of the most important consideration. Nowadays, various security solutions are used as such problems countermeasure; IDS, Firewall and VPN. However, basically internet has much vulnerability of protocol itself. Specially, it is possible to establish a covert channel using TCP/IP header fields such as identification, sequence number, acknowledge number, timestamp and so on. In this Paper, we focus cm the covert channels using identification field of IP header and the sequence number field of TCP header. To detect such covert channels, we used Support Vector Machine which has excellent performance in pattern classification problems. Our experiments showed that proposed method could discern the abnormal cases(including covert channels) from normal TCP/IP traffic using Support Vector Machine.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.545-552
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• 2012

The rapid development of information technology is making large changes in our lives today. Also the infrastructure and services are combinding with information technology which predicts another huge change in our environment. However, the development of information technology brings various types of side effects and these side effects not only cause financial loss but also can develop into a nationwide crisis. Therefore, the detection and quick reaction towards these side effects is critical and much research is being done. Intrusion detection systems can be an example of such research. However, intrusion detection systems mostly tend to focus on judging whether particular traffic or files are malicious or not. Also it is difficult for intrusion detection systems to detect newly developed malicious codes. Therefore, this paper proposes a method which determines whether the present network model is normal or abnormal by comparing it with past network situations.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.396-405
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• 2020

In this study, we used the big data of Voice of Customer (VOC) related to high-speed Internet products to look at the causes of perceived quality and the possibility of proactive service. In order to verify the possibility of proactive service, we collected VOC data from 13 facilities and equipment of a mobile communication service company, and then conducted 𝒙² test to verify that there was a statistically significant difference between the actual VOC observation values and expected values. We found statistical evidence that proactive service is possible through real-time monitoring for the six disability alarms among the 13 facilities and equipment, which are FTTH-R Equipment ON/OFF, FTTH-EV Line Error Detection, Port Faulty, FTTH-R Line Error Detection, Network Loop Detection, and Abnormal Limiting Traffic. Companies are able to adopt the proactive service to improve their market share and to reduce customer service costs. The results of this study are expected to contribute to the actual application of industry in that it has diagnosed the possibility of proactive service in the telecommunication service sector and further suggested suggestions on how to provide effective proactive service.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1043-1057
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• 2015

In order to recognize intelligent threats quickly and detect and respond to them actively, major public bodies and private institutions operate and administer an Intrusion Detection Systems (IDS), which plays a very important role in finding and detecting attacks. However, most IDS alerts have a problem that they generate false positives. In addition, in order to detect unknown malicious codes and recognize and respond to their threats in advance, APT response solutions or actions based systems are introduced and operated. These execute malicious codes directly using virtual technology and detect abnormal activities in virtual environments or unknown attacks with other methods. However, these, too, have weaknesses such as the avoidance of the virtual environments, the problem of performance about total inspection of traffic and errors in policy. Accordingly, for the effective detection of intrusion, it is very important to enhance security monitoring, consequentially. This study discusses a plan for the reduction of false positives as a plan for the enhancement of security monitoring. As a result of an experiment based on the empirical data of G, rules were drawn in three types and 11 kinds. As a result of a test following these rules, it was verified that the overall detection rate decreased by 30% to 50%, and the performance was improved by over 30%.

• Journal of Environmental Science International
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• v.20 no.7
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• pp.799-809
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• 2011

Objectives: This study is aimed at identifying the influential factors on the pulmonary function of ordinary residents in the surrounding areas of Yeosu Industrial Complex. Methods: The PFT (Pulmonary Function Test) was conducted on the target residents numbering 989 people (male 361, female 628). The exposed group (813 people) resided within the radius of 5km from Yeosu Industrial Complex and the control group (176 people) resided in the radius of more than 15 km from May 2007 to November 2007. The survey also took into account other factors including personal characteristics, life habits, respiratory diseases and allergic symptoms, medical histories, and the living environments of the residents in order to further identify influential factors on pulmonary function. Result: When comparing the PFT values of the exposure groups to the control group of the same city, values of the exposure groups were meaningfully lower with an %$FEV_1$ of 107.05% and %FVC of 100.28%. Conversely, the control group reported an %$FEV_1$ and %FVC of 107.26% and 102.85% respectively, indicating that ambient air pollutants reduce lung function. The odds ratio of asthma diagnosis history increased when a subjects residence was close to a heavily trafficked road, traffic amount was huge, a bed was used, and the family had less than four members. However the results were not statistically meaningful. The odds ratios of abnormal pulmonary function were statistically higher among those with asthma(OR=4.29, CI=1.75-10.56), wheezing (OR=2.59, CI=1.24-5.41), and nasal congestion (OR=2.87, CI=1.36-6.08) (p<0.01). The factors affecting $FEV_1$ were symptoms including asthma, passive smoking and allergic eye disease ($R^2$=0.049, p<0.001). For the FVC symptoms including asthma ($R^2$=0.014, p<0.001) were measured. The analysis showed that FVC decreased with increases in $O_3$ and CO(p<0.01). Furthermore, $FEV_1$ decreased with increases in $O_3$(p<0.01). Conclusions: These results will provide preliminary data for establishing responsive measures to protect the health of residents in industrial complexes from air pollution, and to develop lasting environmental health policies.