• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.32-38
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• 2001

Although the ultrasonic method has been developed and used widely in the fields, it has been used only for measuring the defect size and thickness loss. In this study, the relationship between surface wave attenuation through micro-crack growth and variation of velocity under repeated cyclic loading has been investigated. The specimens are adopted from 2.25Cr-1Mo steel, which is used for power plant and pipeline system, and have dimensions of $200{\times}40{\times}4mm$. The results of ultrasonic test with a 5MHz transducer show that surface wave velocity gradually decreases from the point of 60% of fatigue life and the crack length of 2mm with the increasing fatigue cycles. From the results of this study, it is found that the technique using the ultrasonic velocity change is one of very useful methods to evaluate the fatigue life nondestructively.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.494-498
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• 2004

Fire accidents often happen in large buildings because large buildings are equipped with heavy electrical wiring and piping. When fire is to be occurred in those buildings, it is very dangerous to People and building structures. Therefore, multi-point wireless temperature sensors for large buildings are necessary in order to detect fire in the early time and thus to minimize the loss. A wireless device was composed of the transmitter and receiver. The specification of this device was as follows: 915MHz of transmitted frequency, 4 channels, 9600bps of the transmitted speed, and 10mW of the transmitted power. We confirmed through experiment that the temperature was well sensed and fire location was determined by the 4 channel sensors of the developed sensor system.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.63-69
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• 2006

The optical submarine cable has a long distance cable and the repeater for optical amplification compared to territorial optical cable so conventional OTDR utilization for the optical submarine cable is limited. in case the optical core in the optical submarine cable system cut, by using Coherent OTDR that utilize OTDR path in repeater the cable fault point can be detected and in case the faulty of the copper tube in the cable that provide power for the repeater to amplify optical signal, the ways using the current/voltage characteristic, the capacitance per Km and so on is required. this report suggest efficient fault location detecting mechanism by categorized cable fault type.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.69-74
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• 2021

Internet access is also increasing as online activities increase due to the influence of Corona 19. However, network attacks are also diversifying by malicious users, and DDoS among the attacks are increasing year by year. These attacks are detected by intrusion detection systems and can be prevented at an early stage. Various data sets are used to verify intrusion detection algorithms, but in this paper, CICIDS2017, the latest traffic, is used. DDoS attack traffic was analyzed using the decision tree. In this paper, we analyzed the traffic by using the decision tree. Through the analysis, a decisive feature was found, and the accuracy of the decisive feature was confirmed by proceeding the decision tree to prove the accuracy of detection. And the contents of false positive and false negative traffic were analyzed. As a result, learning the feature and the two features showed that the accuracy was 98% and 99.8% respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Convergence Security Journal
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• v.15 no.6_2
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• pp.3-10
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• 2015

Recent massive data breaches or major security incidents show that threats posed by insiders have greatly increased over time. Especially, authorized insiders can cause more serious problems than external hackers can. Therefore there is a growing need to introduce a system that can monitor the insider threats in real time and prevent data breaches or security incidents in early-stage. In this paper, we propose a EITMS(Enterprise Insider-Threats Management System). EITMS detects the abnormal behaviors of authorized insiders based on the normal patterns made from their roles, duties and private activities. And, in order to prevent breaches and incidents in early-stage, a scoring system that can visualize the insider threats is also included.

• The Journal of Korea Assosiation for Disability and Oral Health
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• v.3 no.2
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• pp.87-90
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• 2007

It's important that detect early caries of deciduous and permanent teeth to prevent dental caries and prevserve teeth, especially on proximal surface of deciduous teeth. The reason is that their prominent pulp horn lead to pulp treatment easily due to rapid caries progression. There are conventional exploring, visual inspection and radiographic exam for early caries detection. But, the standard method for diagnosing dental caries is subject and cavitation may be accelerated during exploring procedure. Caries can be diagnosed up to 40% mineral loss with radiograph. $DIFOTI^{(R)}$ (Digital Imaging Fiber-Optic TransIllumination) is diagnostic imaging system for early caries detection using fiber-optic illumination. It is possible that remineralize the tooth surface without tooth preparation and conserve the tooth structure by using $DIFOTI^{(R)}$.