A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning

Keyphrases

• High Performance 100%
• Deep Learning 100%
• IoT Environment 100%
• Secure Architecture 100%
• Hybrid CNN 100%
• Safety Issues 50%
• Increased Reliability 50%
• Cyber-attacks 50%
• Distributed Denial of Service (DDoS) 50%
• False Positive Rate 50%
• Reconnaissance 50%
• Packet Size 50%
• IoT Data 50%
• Deep Learning Model 50%
• Increasing Risk 50%
• IoT Security 50%
• Low False Alarm Rate 50%
• Spatial Features 50%
• Threat Detection 50%
• CNN Layers 50%
• Adversarial Attack 50%
• Botnet 50%
• Secure Framework 50%
• Data Exfiltration 50%
• LSTM Layer 50%
• Real-time Intrusion Detection 50%
• CNN-LSTM 50%
• Temporal Dependency 50%
• Connectivity Protocols 50%
• LSTM-CNN Model 50%
• LSTM-GRU 50%
• Feature Analysis 50%
• Connection Length 50%

Computer Science

• Internet-Of-Things 100%
• Deep Learning Method 100%
• Long Short-Term Memory Network 100%
• Convolutional Neural Network 100%
• Cyber Attack 16%
• Recurrent Neural Network 16%
• False Positive Rate 16%
• Intrusion Detection 16%
• Deep Learning Model 16%
• False Alarm Rate 16%
• Threat Detection 16%
• Adversarial Machine Learning 16%
• Botnets 16%
• Bidirectional Long Short-Term Memory Network 16%
• Data Exfiltration 16%
• Reconnaissance 16%
• SHapley Additive exPlanation 16%
• LSTM Layer 16%