Search Results - (( data distributions _ algorithm ) OR ( based detection system algorithm ))
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1
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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2
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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3
High Impedance Fault Detection on Power Distribution Feeder
Published 2012“…The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power system.…”
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4
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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5
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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6
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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7
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2023“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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8
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System
Published 2012“…The algorithm for HIF detection based on the amplitude ratio of second and odd harmonics to fundamental is presented. …”
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9
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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10
E2IDS: an enhanced intelligent intrusion detection system based on decision tree algorithm
Published 2022“…The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. …”
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11
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model
Published 2013“…This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. …”
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12
Broken Conductor Detection on Power Distribution Feeder
Published 2013“…This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. …”
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13
Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…In this work, a new islanding detection technique is proposed based on combination of Slantlet Transform and Ridgelet Probabilistic Neural Network to detect islanding conditions from other disturbance for a 250-kW PV array connected to a typical North American distribution grid and a wind farm power generation system. …”
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14
Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi
Published 2020“…The system is simulated using EPANET (Environmental Prediction Agency Network) software based on actual water distribution system (WDS) data from Benghazi city, Libya. …”
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15
Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Thus, using neural network-based semi-supervised stream data learning is inadequate due to capture the changes in the distribution and characteristics of various classes of data while avoiding the effect of the outdated stored knowledge in neural networks (NN). …”
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16
Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. …”
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Design and analysis of management platform based on financial big data
Published 2023“…In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. …”
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18
Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems
Published 2021“…Another problematic aspect is related to the intrusion detection solutions that are based on machine learning cluster algorithms to learn systems’ specifications and extract general state-based rules for attacks identification. …”
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19
A novel discrete wavelet transform-based graphical language classifier for identification of high-impedance fault in distribution power system
Published 2020“…This paper proposes a discrete wavelet transform (DWT)-based Graphical Language classifier algorithm for identification of high-impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. …”
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20
Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…Deep learning algorithms were widely used among all the data-driven algorithms. …”
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