• Journal of Communications and Networks
• /
• v.10 no.1
• /
• pp.89-97
• /
• 2008

Anomaly detection systems playa significant role in protection mechanism against attacks launched on a network. The greatest challenge in designing systems detecting anomalous exploits is defining what to measure. Effective yet simple, Shannon entropy metrics have been successfully used to detect specific types of malicious traffic in a number of commercially available IDS's. We believe that Renyi entropy measures can also adequately describe the characteristics of a network as a whole as well as detect abnormal traces in the observed traffic. In addition, Renyi entropy metrics might boost sensitivity of the methods when disambiguating certain anomalous patterns. In this paper we describe our efforts to understand how Renyi mutual information can be applied to anomaly detection as an offline computation. An initial analysis has been performed to determine how well fast spreading worms (Slammer, Code Red, and Welchia) can be detected using our technique. We use both synthetic and real data audits to illustrate the potentials of our method and provide a tentative explanation of the results.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.6
• /
• pp.277-283
• /
• 2021

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1071-1082
• /
• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.26 no.4
• /
• pp.310-322
• /
• 2022

In this report, we apply an anomaly detection algorithm to a mobile oral health care application. In particular, we have investigated one class YOLOv3 as an anomaly detection model to classify pictures of mouths which will be used as inputs in the following machine learning model. We have achieved outstanding performances by proposing appropriate annotation strategies for our data sets and modifying the loss function. Moreover, the model can classify not only oral and non-oral pictures but also output preprocessed pictures that only contain the area around the lips by using the predicted bounding box. Thus, the model performs prediction and preprocessing simultaneously.

• International journal of advanced smart convergence
• /
• v.4 no.2
• /
• pp.46-53
• /
• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• Economic and Environmental Geology
• /
• v.32 no.6
• /
• pp.645-651
• /
• 1999

The gravity and magnetic measurements have been obtained from 34 stations with 50m intervals along the survey line positioned between Jangtanri and sindapri for studying subsurface geology and structures of the volcanic rocks in Yeoncheon area. The Bouguer gravity and magnetic anomaly values were evaluated from the reduction of the field observation, and then interpreted by Nettleton's method and maximum-pepth rules, are approximately 160m based on magnetic data and 135m based on gravity data. High Bouguer gravity anomaly zone between 0m in Jangtanri and 900m along the survery line, is caused by thick and high density, older dasalt which is positioned beneath jijangbong tuff breccia, and this result corresponds to the interpretation result based on magnetic anomly. Lower gravity and magnetic anomaly zones ariund 900m are caused by between 1300m and 1550m are caused by high density of Quarternary basalt exposed in the surface, and lower gravity and magnetic anomalies at 200m and 1250m are caused by faults.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.317-322
• /
• 2002

Dramatic changes in the patterns of satellite-derived pigment concentrations, sea-level height anomaly, sea surface temperature anomaly, and zonal wind anomaly are observed during the 1997-1998 El Nino. By some measures, the 1997-1998 El Nino was the strongest of the 20$^{th}$ century. A very strong El Nino developed during 1997 and matured late in the year. A dramatic recovery occurred in mid-1998 and led to a La Nina conditions. The largest spatial extent of the phytoplankton bloom was followed recovery from El Nino over the equatorial Pacific. The evolution towards a warm episode (El Nino) continued in the equatorial Pacific from March 2002 and further development toward mature El Nino conditions may be possible in late 2002. The OSMI (Ocean Scanning Multispectral Imager) data can be used for detection of dramatic changes in the patterns of pigment concentration during next El Nino.

• Journal of Korean Society of Water and Wastewater
• /
• v.38 no.2
• /
• pp.83-93
• /
• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

• Journal of the Korea Convergence Society
• /
• v.12 no.2
• /
• pp.1-6
• /
• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.157-163
• /
• 2022

Recently, with the development of automation in the industrial field, research on anomaly detection is being actively conducted. An application for anomaly detection used in factory automation is camera-based defect inspection. Vision camera inspection shows high performance and efficiency in factory automation, but it is difficult to overcome the instability of lighting and environmental conditions. Although camera inspection using deep learning can solve the problem of vision camera inspection with much higher performance, it is difficult to apply to actual industrial fields because it requires a huge amount of normal and abnormal data for learning. Therefore, in this study, we propose a network that overcomes the problem of collecting abnormal data with 72 geometric transformation deep learning methods using only normal data and adds an outlier exposure method for performance improvement. By applying and verifying this to the MVTec data set, which is a database for auto-mobile parts data and outlier detection, it is shown that it can be applied in actual industrial sites.