Výsledky vyhledávání - improved conditional variational autoencoder (ICVAE)

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Autoři: Yanqing Yang Kangfeng Zheng Chunhua Wu a další

Zdroj: Sensors, Vol 19, Iss 11, p 2528 (2019)

Témata: intrusion detection, variational inference, improved conditional variational autoencoder, generator network, deep neural network, Chemical technology, TP1-1185

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1424-8220/19/11/2528; https://doaj.org/toc/1424-8220

Přístupová URL adresa: https://doaj.org/article/992c6ab3bdf848209c95c98257738587

On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples.

Autoři: Zhang, Ying Liu, Qiang

Zdroj: Future Generation Computer Systems. Aug2022, Vol. 133, p213-227. 15p.

Témata: *DISTRIBUTION (Probability theory), INTRUSION detection systems (Computer security), DATA augmentation, INTERNET of things, LATENT variables, FEATURE extraction, DIMENSION reduction (Statistics)

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network.

Autoři: Yang, Yanqing Zheng, Kangfeng Wu, Chunhua a další

Zdroj: Sensors (14248220); Jun2019, Vol. 19 Issue 11, p2528, 1p

Témata: ARTIFICIAL neural networks, GLOBAL optimization, MACHINE learning

ICVAE: Interpretable Conditional Variational Autoencoder for De Novo Molecular Design.

Autoři: Fan, Xiaqiong Fang, Senlin Li, Zhengyan a další

Zdroj: International Journal of Molecular Sciences; May2025, Vol. 26 Issue 9, p3980, 14p

Témata: DRUG discovery, AUTOENCODERS, OPTIMIZATION algorithms, LINEAR operators, VALUATION of real property

基于高斯混合聚类和改进条件变分自编码的 多风电场功率日场景生成方法. (Chinese)

Alternate Title: Daily Power Scenario Generation Method for Multiple Wind Farms Based on Gaussian Mixture Clustering and Improved Conditional Variational Autoencoder. (English)

Autoři: 李丹 梁云嫣 缪书唯 a další

Zdroj: Electric Power; Dec2024, Vol. 57 Issue 12, p17-29, 13p

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks.

Autoři: He, Jiaxing Wang, Xiaodan Song, Yafei a další

Zdroj: Applied Intelligence; May2023, Vol. 53 Issue 10, p12416-12436, 21p

Témata: GENERATIVE adversarial networks, CONVOLUTIONAL neural networks, CONTINUOUS distributions, ANOMALY detection (Computer security), GAUSSIAN distribution

Imbalanced rock burst assessment using variational autoencoder-enhanced gradient boosting algorithms and explainability.

Autoři: Shan Lin Zenglong Liang Miao Dong a další

Zdroj: Underground Space (2096-2754); Aug2024, Vol. 17, p226-245, 20p

Témata: ROCK bursts, ARTIFICIAL intelligence, STATISTICAL ensembles, SENSITIVITY analysis, RESAMPLING (Statistics), AUTOENCODERS

Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT).

Autoři: Khanam, Shapla Ahmedy, Ismail Idris, Mohd Yamani Idna a další

Zdroj: Sensors (14248220); Aug2022, Vol. 22 Issue 15, p5822-N.PAG, 22p

Témata: INTRUSION detection systems (Computer security), ARTIFICIAL neural networks, INTERNET of things

Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders.

Autoři: Pesteie, Mehran Abolmaesumi, Purang Rohling, Robert N.

Zdroj: IEEE Transactions on Medical Imaging; Dec2019, Vol. 38 Issue 12, p2807-2820, 14p

Témata: MAGNETIC resonance imaging, LATENT variables, ACQUISITION of data, ULTRASONIC imaging, DATA distribution

AI-Driven Information Reconstruction: Bridging Visual Perception and Memory. (English)

Autoři: Zhang, Guoxin Wang, Wenqiang Li, Feng a další

Zdroj: Psychological Science; Nov2025, Vol. 48 Issue 6, p1346-1358, 13p

Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study.

Autoři: Javadi, Golara Samadi, Samareh Bayat, Sharareh a další

Zdroj: International Journal of Computer Assisted Radiology & Surgery; Jun2020, Vol. 15 Issue 6, p1023-1031, 9p

Tıbbi Görüntülemede Derin Üretken Modeller: Bir Literatür Taraması.

Alternate Title: Deep Generative Models in Medical Imaging: A Literature Review. (Turkish)

Autoři: ŞENER, Begüm

Zdroj: Journal of Polytechnic; mar2025, Vol. 28 Issue 2, preceding p413-433, 22p

Témata: GENERATIVE adversarial networks, DATA augmentation, COMPUTER-assisted image analysis (Medicine), DIAGNOSTIC imaging, DEEP learning

Enhancing intrusion detection: protocol-based security using a hybrid RIDGE classifier on InSDN, UNSW-NB15, BoT-IoT, and ToN-IoT datasets.

Autoři: Nemalikanti, Anand Kaki, Sreenija Ambati, Rami Reddy a další

Zdroj: Cluster Computing; Oct2025, Vol. 28 Issue 10, p1-26, 26p

Témata: INTRUSION detection systems (Computer security), INTERNET of things, DATA transformations (Statistics), DEEP learning, SOFTWARE-defined networking, DATA libraries, EVALUATION methodology

Intelligent Detection of Abnormal Traffic Based on SCN-BiLSTM.

Autoři: Zhang, Lulu Du, Xuehui Wang, Wenjuan a další

Zdroj: Computers, Materials & Continua; 2025, Vol. 84 Issue 1, p1901-1919, 19p

Témata: CONVOLUTIONAL neural networks, LONG short-term memory, TRAFFIC monitoring, FEATURE extraction, GENERALIZATION

Sfedrl-ids: secure federated deep reinforcement learning-based intrusion detection system for agricultural internet of things.

Autoři: Benameur, Rabaie Dahane, Amine

Zdroj: Cluster Computing; Oct2025, Vol. 28 Issue 6, p1-20, 20p

Témata: ARTIFICIAL neural networks, REINFORCEMENT learning, DEEP reinforcement learning, ARTIFICIAL intelligence, LONG short-term memory

Synthesizing Realistic Knee MRI Images: A VAEGAN Approach for Enhanced Medical Data Augmentation.

Autoři: S. A., Revathi Babu, B. Sathish

Zdroj: International Journal of Advanced Computer Science & Applications; 2024, Vol. 15 Issue 11, p792-799, 8p

Intrusion detection based on generative adversarial network with random forest for cloud networks.

Autoři: Rosline, Gnanam Jeba Rani, Pushpa

Zdroj: International Journal of Electrical & Computer Engineering (2088-8708); Apr2025, Vol. 15 Issue 2, p2491-2498, 8p

Témata: GENERATIVE adversarial networks, CLOUD computing security measures, RANDOM forest algorithms, ANOMALY detection (Computer security), CLOUD forests, INTRUSION detection systems (Computer security)

Online Network DoS/DDoS Detection: Sampling, Change Point Detection, and Machine Learning Methods.

Autoři: Owusu, Evans Rahouti, Mohamed Kumar Jagatheesaperumal, Senthil a další

Zdroj: IEEE Communications Surveys & Tutorials; Aug2025, Vol. 27 Issue 4, p2543-2580, 38p

A Hybrid Three-Layered Approach for Intrusion Detection Using Machine Learning Methods.

Autoři: Beigi, A.

Zdroj: Journal of Electrical & Computer Engineering Innovations (JECEI); Jul2025, Vol. 13 Issue 2, p443-454, 12p

Témata: COMPUTER network security, COMPUTER networks, MACHINE learning, INSTRUCTIONAL systems, INTRUSION detection systems (Computer security)

Recent advances in deep learning models: a systematic literature review.

Autoři: Malhotra, Ruchika Singh, Priya

Zdroj: Multimedia Tools & Applications; Dec2023, Vol. 82 Issue 29, p44977-45060, 84p