• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2171-2180
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• 2011

Security is a significant concern for many sensor network applications. Intrusion detection is one method of defending against attacks. However, standard intrusion detection techniques are not suitable for sensor networks with limited resources. In this paper, propose a new method for selecting and managing the detect nodes in IDS(intrusion detection system) for anomaly detection in sensor networks and the node scheduling technique for maximizing the IDS's lifetime. Using the genetic algorithm, developed the solutions for suggested optimization equation and verify the effectiveness of proposed methods by simulations.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.219-224
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• 2007

We conducted geophysical surveys to detect antitank mine at Namji-eup, Gyeongsangnam-do which had been installed during Korean war. The surveys consisted of 2 stages, at the first stage we divided the survey area into 7 block and carried out magnetic gradient survey and GEM-3 EM survey sequentially for each block. Hence we verified anomaly areas using an excavator and a metal detector. Most of anomalies were found to be garbages such as trash cans, metallic wastes, and so on. And also, the concrete pipe was found at depth of 1 m, which had not referred in any report of that area. At the second stage, after trenching the covered soil down to 75 cm the same surveys were conducted. We could not find the strong signal to be inferred from a antitank mine, but we pointed out some anomalies to need careful handling because demining is very dangerous work even though there is few possibility that is mine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Archives of Craniofacial Surgery
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• v.24 no.4
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• pp.145-158
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• 2023

Vascular anomalies encompass a variety of malformations and tumors that can result in severe morbidity and mortality in both adults and children. Advances have been made in the classification and diagnosis of these anomalies, with the International Society for the Study of Vascular Anomalies establishing a widely recognized classification system. In recent years, notable progress has been made in genetic testing and imaging techniques, enhancing our ability to diagnose these conditions. The increasing sophistication of genetic testing has facilitated the identification of specific genetic mutations that help treatment decisions. Furthermore, imaging techniques such as magnetic resonance imaging and computed tomography have greatly improved our capacity to visualize and detect vascular abnormalities, enabling more accurate diagnoses. When considering reconstructive surgery for facial vascular anomalies, it is important to consider both functional and cosmetic results of the procedure. Therefore, a comprehensive multidisciplinary approach involving specialists from dermatology, radiology, and genetics is often required to ensure effective management of these conditions. Overall, the treatment approach for facial vascular anomalies depends on the type, size, location, and severity of the anomaly. A thorough evaluation by a team of specialists can determine the most appropriate and effective treatment plan.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.506-515
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• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.67-76
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• 2024

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.