The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
Hadis Abarghouei - One of the best experts on this subject based on the ideXlab platform.
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Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2020Co-Authors: Mohammad Rasoul Tanhatalab, Hossein Yousefi, Hesam Mohammad Hosseini, Mostafa Mofarah Bonab, Vahid Fakharian, Hadis AbarghoueiAbstract:In this paper, we propose a novel algorithm to Detect Anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. Hence, adopting R-CNN provides us with spatial-temporal analysis. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4. 5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffics are extremely important for the owner of wireless networks, because it is directly related to the revenue, and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to especial events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to Detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of Anomaly Detection. However, the proposed algorithm is capable of Detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends, and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to Detect anomalies in their live networks in order to keep the KPIs in normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 thousand.
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Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
arXiv: Signal Processing, 2019Co-Authors: Mohammad Rasoul Tanhatalab, Hossein Yousefi, Hesam Mohammad Hosseini, Mostafa Mofarah Bonab, Vahid Fakharian, Hadis AbarghoueiAbstract:In this paper, we propose a novel algorithm to Detect Anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4.5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffic are extremely important for the owner of wireless networks because it is directly related to the revenue and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to special events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to Detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of Anomaly Detection. However, the proposed algorithm is capable of Detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to Detect anomalies in their live networks in order to keep the KPIs in a normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 cells.
Yongzhi Lai - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder
IEEE Access, 2020Co-Authors: Tingting Chen, Xueping Liu, Bizhong Xia, Wei Wang, Yongzhi LaiAbstract:With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically Detect incipient failures before robots totally shut down. Model-based methods are typically used in Anomaly Detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for Anomaly Detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised Anomaly Detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time Anomaly Detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully Detect Anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.
Yun Wang - One of the best experts on this subject based on the ideXlab platform.
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develop a composite risk score to Detect Anomaly intrusion
SoutheastCon, 2005Co-Authors: Yun Wang, J CannadyAbstract:This study developed a composite risk score for Anomaly intrusion Detection based on the KDD-cup 1999 data, which demonstrated a high sensitivity, specificity and a low misclassification in Detecting network attacks (0.90, 0.94, and 0.08, respectively). This score provides a statistically sound basis for developing a real time intrusion Detection system, and it can be constructed via common computer languages without interfacing with any typical software and statistical tool.
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A multinomial logistic regression modeling approach for Anomaly intrusion Detection
Computers & Security, 2005Co-Authors: Yun WangAbstract:Although researchers have long studied using statistical modeling techniques to Detect Anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for Detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in Detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to Detect Anomaly intrusion.
Mohammad Rasoul Tanhatalab - One of the best experts on this subject based on the ideXlab platform.
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Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2020Co-Authors: Mohammad Rasoul Tanhatalab, Hossein Yousefi, Hesam Mohammad Hosseini, Mostafa Mofarah Bonab, Vahid Fakharian, Hadis AbarghoueiAbstract:In this paper, we propose a novel algorithm to Detect Anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. Hence, adopting R-CNN provides us with spatial-temporal analysis. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4. 5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffics are extremely important for the owner of wireless networks, because it is directly related to the revenue, and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to especial events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to Detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of Anomaly Detection. However, the proposed algorithm is capable of Detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends, and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to Detect anomalies in their live networks in order to keep the KPIs in normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 thousand.
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Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
arXiv: Signal Processing, 2019Co-Authors: Mohammad Rasoul Tanhatalab, Hossein Yousefi, Hesam Mohammad Hosseini, Mostafa Mofarah Bonab, Vahid Fakharian, Hadis AbarghoueiAbstract:In this paper, we propose a novel algorithm to Detect Anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4.5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffic are extremely important for the owner of wireless networks because it is directly related to the revenue and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to special events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to Detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of Anomaly Detection. However, the proposed algorithm is capable of Detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to Detect anomalies in their live networks in order to keep the KPIs in a normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 cells.
Tingting Chen - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder
IEEE Access, 2020Co-Authors: Tingting Chen, Xueping Liu, Bizhong Xia, Wei Wang, Yongzhi LaiAbstract:With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically Detect incipient failures before robots totally shut down. Model-based methods are typically used in Anomaly Detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for Anomaly Detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised Anomaly Detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time Anomaly Detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully Detect Anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.