Hybrid Anomaly Detection Model on Trusted IoT Devices

Keyphrases

• Internet of Things Devices 100%
• Hybrid Anomaly 100%
• Hybrid Model 75%
• Machine Learning 25%
• Deep Learning Methods 25%
• Computational Resources 25%
• Data Cleaning 25%
• Communication Channels 25%
• Black Box 25%
• Anomaly Detection 25%
• Internet of Things 25%
• Sensor Data 25%
• Process Data 25%
• Energy Resources 25%
• Cloud Server 25%
• Unsupervised Learning 25%
• Malicious Users 25%
• External Data 25%
• Bandwidth Enhancement 25%
• Acquiring Data 25%
• Smoothing Filter 25%
• Computational Memory 25%
• Trusted Data 25%
• AVR Microcontroller 25%
• Implementation Cost 25%
• Internet of Things Environment 25%
• Tensilica 25%
• Memory Footprint 25%
• Additional Energy 25%
• Hardware Requirements 25%
• ARM Processor 25%

Computer Science

• Internet of Things Device 100%
• Anomaly Detection 100%
• Data Cleaning 50%
• Internet-Of-Things 50%
• Data Source 25%
• Microcontroller 25%
• Computational Resource 25%
• Hardware Requirement 25%
• Process Data 25%
• Unsupervised Learning 25%
• Acquiring Data 25%
• Memory Footprint 25%
• Deep Learning Technique 25%
• Machine Learning 25%
• Learning System 25%
• Data Collection 25%

Earth and Planetary Sciences

• Machine Learning 100%
• Data Acquisition 100%