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Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models learning methods for detecting anomalies. Semi-supervised anomaly detection uses only instances of normal
Разработка ML-подхода идентификации аномалий по логам компьютерных систем с помощью методов обработки естественного языкаThis work is devoted to the important problem of detecting anomalies in computer systems using
Self-supervised Algorithms for Anomaly Detection on X-Rays. In this paper we consider only self-supervised anomaly detection algorithms. We are using several architectures
Mobile network traffic analysis based on probability-informed machine learning approach based on GG distribution for detecting suspected anomalies in traffic. © 2024 Elsevier B.V.
Anomaly detection for short texts: Identifying whether your chatbot should switch from goal-oriented conversation to chit-chatting if we consider it as an anomaly detection task which is in a field of machine learning. The scientific
Anomaly Electrocardiograms Automatic Detection with Unsupervised Deep Learning MethodsAnomaly detection is an important problem in various fields of technology and industry
Time-frequency analysis and autoencoder approach for network traffic anomaly detectionDetection of anomalies in network traffic is critical to mitigating cyber threats. This study
Detecting anomalies in network traffic using machine learning techniquesThe problem of anomaly detection in network traffic using machine learning and neural network
A new approach for anomaly detection in web applications in the accuracy of anomaly detection and reduction of the rate of incorrect detections.
Fault Detection in the Gas Turbine of the Kirkuk Power Plant: An Anomaly Detection Approach Using DLSTM-Autoencoder hidden layers for fault occurrence prediction by considering an anomaly detection approach. To this end
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