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{ "item_title" : "Deep Learning for Security and Privacy Preservation in Iot", "item_author" : [" Aaisha Makkar", "Neeraj Kumar "], "item_description" : "Metamorphosis of Industrial IoT using Deep Leaning.- Deep Learning Models and their Architectures for Computer Vision Applications: A Review.- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures.- A Review on Cyber Crimes on the Internet of Things.- Deep learning framework for anomaly detection in IoT enabled systems.- Anomaly Detection using Unsupervised Machine Learning Algorithms.- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT.- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario.- Deep learning Models: An Understandable Interpretable Approaches.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/81/166/185/9811661855_b.jpg", "price_data" : { "retail_price" : "219.99", "online_price" : "219.99", "our_price" : "219.99", "club_price" : "219.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Learning for Security and Privacy Preservation in Iot|Aaisha Makkar

Deep Learning for Security and Privacy Preservation in Iot

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Overview

Metamorphosis of Industrial IoT using Deep Leaning.- Deep Learning Models and their Architectures for Computer Vision Applications: A Review.- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures.- A Review on Cyber Crimes on the Internet of Things.- Deep learning framework for anomaly detection in IoT enabled systems.- Anomaly Detection using Unsupervised Machine Learning Algorithms.- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT.- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario.- Deep learning Models: An Understandable Interpretable Approaches.

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Details

  • ISBN-13: 9789811661853
  • ISBN-10: 9811661855
  • Publisher: Springer
  • Publish Date: April 2022
  • Dimensions: 9.21 x 6.14 x 0.5 inches
  • Shipping Weight: 0.99 pounds
  • Page Count: 179

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