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An anomaly detection framework for IoT devices through deep learning / Ayesha Aslam. [Thesis or Dissertation]

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Material type: Text

TextLanguage: English Publication details: Islamabad : COMSATS University Islamabad, 2021.Description: xv, 94 pages : illustrations ; 30 cm +Subject(s):

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Dissertation note: COMSATS University Islamabad

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Holdings
Item type	Current library	Collection	Call number	URL	Copy number	Status	Date due	Barcode	Item holds
Thesis or Dissertation	Junaid Zaidi Library, COMSATS University Islamabad 2nd Floor	Thesis or Dissertation	T 004 ASL-A 11544 (Browse shelf(Opens below))	Link to resource	1	Available		10001TP0011544

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Previous	T 004 ASI-I 8712 Implication of meta learning algorithm for software fault prediction /	T 004 ASI-T 11344 Towards energy efficient smart grids data augmentation through BiWGAN, feature extraction and classification using hybrid 2DCNN and BiLSTM /	T 004 ASL-A 10219 A novel combined and game - theoretic approach to avoid smart grids from energy stealing /	T 004 ASL-A 11544 An anomaly detection framework for IoT devices through deep learning /	T 004 ASL-E 11427 Evolutionary based ensemble for multi-label classification /	T 004 ATE-M 10731 Multi-parametric analysis of QoS in wireless sensor networks a data driven approach /	T 004 ATE-M 10731 Multi-parametric analysis of QoS in wireless sensor networks a data driven approach /	Next

COMSATS University Islamabad

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