• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.159-164
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• 2024

This study aimed to develop an advanced anomaly-detection system tailored for solar power distribution panels using thermal imaging cameras to ensure operational stability. It addresses the imperative shift toward digitalized safety management in electrical facilities, transcending the limitations of conventional empirical methodologies. Our proposed system leverages a faster R-CNN-based artificial intelligence model optimized through meticulous hyperparameter tuning to efficiently detect anomalies in distribution panels. Through comprehensive experimentation, we validated the efficacy of the system in accurately identifying anomalies, thereby propelling safety protocols forward during the fourth industrial revolution. This study signifies a significant stride toward fortifying the integrity and resilience of solar power distribution systems, which is pivotal for adapting to emerging technological paradigms and evolving safety standards in the energy sector. These findings offer valuable insights for enhancing the reliability and efficiency of safety management practices and fostering a safer and more sustainable energy landscape.

• 정보처리학회논문지C
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• 제12C권1호
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• pp.19-28
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• 2005

최근 알려지지 않은 공격(unknown attack)으로부터 네트워크를 보호하기 위한 네트워크 트래픽 어노멀리(anomaly) 검출에 대한 관심이 고조되고 있다. 본 논문에서는 캠퍼스 네트워크의 보드라우터(border router)의 NetFlow 데이터로 제공되는 초당비트수(bits per second)와 초당플로수(flows per second)의 상관관계를 단순회귀분석을 통하여 새로운 어노멀리 검출 기법을 제시하였다. 새로이 제안된 기법을 검증하기 위해 실지 캠퍼스 네트워크에 적용하였으며 그 결과론 Holt-Winters seasonal(HWS) 알고리즘과 비교하였다. 특히, 제안된 기법은 기존 RRDtool에 통합시켜 실시간 검출이 가능하도록 설계하였다.

• 정보보호학회논문지
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• 제13권2호
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• pp.99-106
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• 2003

네트워크 환경에서의 다양한 침입은 심각한 위험을 초래 할 수 있기 때문에 침입을 효과적으로 탐지하기 위해 데이터마이닝 기법을 발전시켜 왔다. 비정상행위 탐지 기술은 순수 데이터로 학습한 후, 비정상행위를 탐지하기 때문에 정교한 정상행위 패턴 생성이 필수적이다. 순수한 학습 데이터의 생성은 시간과 비용이 많이 드는 단점이 있다. 따라서 네트워크 상의 데이터에 대한 특징을 파악하는 것이 중요하다. 본 논문에서는 데이터마이닝의 연관규칙 및 클러스터링기법을 비정상행위 탐지에 적용하였고, 패킷내의 판정 요소에 정보이론 척도를 적용하여 불필요한 데이터를 필터링하는 방법을 제시하였다. 또한 가변길이 트랜잭션을 네트워크상의 분석 단위를 정의하는 기준으로 제시하여 행위 패턴 생성에 보다 묘사성이 높음을 보였다.

• 정보보호학회논문지
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• 제12권5호
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• pp.63-73
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• 2002

최근에 컴퓨터 침입으로 인한 피해가 날로 증가하고 있으며 다양한 침입 기법들이 새롭게 개발되고 있다. 따라서 침입자들의 행위를 효과적으로 탐지하기 위해서 기존의 오용탐지 방법과 더불어 비정상행위 모델의 적용에 대한 많은 연구가 진행되었다. 본 논문에서는 네트워크를 통해서 수신되는 패킷에 대한 정상행위 패턴을 생성하기 위해서 패킷 내 뿐만 아니라 패킷간의 연관성을 탐사하는 새로운 연관 규칙 알고리즘을 제안한다. 이와 더불어 다양한 실험을 통해서 본 논문에서 제안된 비정상행위 판정시스템에서 탐지율을 최대화 할 수 있는 임계치 값들을 제시한다. 결과적으로 효과적인 비정상행위 판정이 가능하다.

• Smart Structures and Systems
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• 제31권2호
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• pp.183-197
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• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.

• ETRI Journal
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• 제45권4호
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1284-1295
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• 2024

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

• 한국정보전자통신기술학회논문지
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• 제5권3호
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• pp.158-163
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• 2012

본 논문에서 나이브 베이지안 알고리즘, 데이터 마이닝, Fuzzy logic을 이용하여 이상 공격과 오용 공격을 탐지하는 하이브리드 침입탐지시스템인 FHIDS(Fuzzy logic based Hybrid Intrusion Detection System)을 설계하였다. 본 논문에서 설계한 FHIDS의 NB-AAD(Naive Bayesian based Anomaly Attack Detection)기법은 나이브 베이지안 알고리즘을 이용해 이상 공격을 탐지하고, DM-MAD(Data Mining based Misuse Attack Detection)기법은 데이터 마이닝 알고리즘을 이용하여 패킷들의 연관 규칙을 분석하여 새로운 규칙기반 패턴을 생성하거나 변형된 규칙 기반 패턴을 추출함으로써, 새로운 공격이나 변형된 공격을 탐지한다. 그리고 FLD(Fuzzy Logic based Decision)은 NB-AAD과 DM-MAD의 결과를 이용하여 정상인지 공격인지를 판별한다. 즉, FHIDS는 이상과 오용공격을 탐지 가능하며 False Positive 비율을 감소시키고, 변형 공격 탐지율을 개선한 하이브리드 공격탐지시스템이다.