Results 171 to 180 of about 5,065 (216)
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AE-DCNN: Autoencoder Enhanced Deep Convolutional Neural Network For Malware Classification
2021 International Conference on Intelligent Technologies (CONIT), 2021Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware
Shashank Kumar +3 more
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PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection
2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020Anomaly detection for textured surface is a key task in product quality control. In recent years, supervised deep learning approaches have begun to be applied in this field, whereas most of the approaches are usually impracticable in collecting a large scale of defect samples. To this end, this paper proposes an efficient pyramid memory autoencoder.
Haiming Yao +3 more
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SF-AE: Split Federated Autoencoder for Unsupervised IoT Intrusion Detection
Smart systems have become increasingly popular in recent years, widening the attack surface of cyber threats. Machine learning algorithms have been successfully integrated into modern security mechanisms to detect such attacks. Internet of Things (IoT) systems often have limited computational resources and are unable to execute entire machine learning ...Augello, Andrea +3 more
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Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin
Applied Soft ComputingAccurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data.
Chengjia Han +4 more
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Neural Networks
Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder
Yueyue Yao +3 more
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Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder
Yueyue Yao +3 more
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AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT
2022 International Telecommunications Conference (ITC-Egypt), 2022Mohamed Mahmoud +3 more
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MoEP-AE: Autoencoding Mixtures of Exponential Power Distributions for Open-Set Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2023Jiayin Sun, Hong Wang, Qiulei Dong
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Comparative Study of Autoencoder and LSTM-AE for Extreme Temperature Anomaly Detection in Semarang
Proceedings of The International Conference on Data Science and Official StatisticsClimate change has increased the frequency and intensity of extreme weather events, including heatwaves and cold spells, posing critical risks to public health and urban infrastructure. This study proposes and compares two deep learning frameworks based on Autoencoders, namely the Long Short-Term Memory Autoencoder (LSTM-AE) and the standard ...
Galih Kusuma Wijaya +4 more
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A comprehensive survey on design and application of autoencoder in deep learning
Applied Soft Computing Journal, 2023Yan Pei, Jianqiang Li
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