Results 21 to 30 of about 93,522 (265)
Machine Learning and Knowledge Extraction, 2023 The proliferation of novel attacks and growing amounts of data has caused practitioners in the field of network intrusion detection to constantly work towards keeping up with this evolving adversarial landscape.
Brian Lewandowski, Randy Paffenroth
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BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder
Mathematics, 2023 In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model.
Dongqi Wang, Mingshuo Nie, Dongming Chen
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Feedback Recurrent Autoencoder [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang, Yang +3 more
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Enhancing Radar Resolution and Target Detection Probability with a Denoising Autoencoder [PDF]
Journal of Electromagnetic Engineering and ScienceWe propose the use of a denoising autoencoder to improve radar resolution and target detection probability in noise-contaminated range-Doppler diagrams. Conventionally, target detection has been performed using constant false alarm rate (CFAR) algorithms,
Wonhyo Kim, Daegun Oh, Youngwook Kim
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A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization
SN Applied Sciences, 2021 Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks.
R. Sekhar +3 more
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Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
, 2018 In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully ...
Mirjalili, Vahid +3 more
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Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results
IEEE AccessThis paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives,
Azin Masoudi, Majid Ahmadi
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, 2018 Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a
Bacciu, Davide +2 more
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Detection of Pitting in Gears Using a Deep Sparse Autoencoder
Applied Sciences, 2017 In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network.
Yongzhi Qu +3 more
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Deep Learning-Based Joint Optimization of Modulation and Power for Nonlinearity-Constrained System
IEEE Access, 2022 For wireless communication systems with a long distance or severe interference, the insufficient transmit power limits the system performance. In this case, the maximum transmit power depends on the nonlinearity and the saturation region of the power ...
Zhiyuan Liu, Meng Ma
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