A Neural Attention Model for Real-Time Network Intrusion Detection

Keyphrases

• Real-time Networks 100%
• Network Intrusion Detection 100%
• Bidirectional Long Short-Term Memory (BiLSTM) 100%
• Neural Attention Model 100%
• Network Attacks 50%
• Computational Efficiency 50%
• False Positive Rate 50%
• Network Traffic 50%
• Feature Extraction 50%
• Attack Detection 50%
• Performance Results 50%
• Conditional Random Field Model 50%
• Network Intrusion Detection System 50%
• Precision-recall 50%
• Transformer Model 50%
• Network Intrusion 50%
• Language Translation 50%
• Intrusion Attacks 50%
• Security Practitioner 50%
• Real-time Attack Detection 50%
• Time-of-use 50%
• Flow-based 50%
• Recurrent Layers 50%
• Neural Attention Mechanism 50%

Computer Science

• Bidirectional Long Short-Term Memory Network 100%
• network intrusion detection 100%
• Long Short-Term Memory Network 50%
• Network Traffic 50%
• Modified Version 50%
• False Positive Rate 50%
• Attention (Machine Learning) 50%
• Conditional Random Field 50%
• Random Field Model 50%
• Network Based Intrusion Detection System 50%
• Network Intrusion 50%
• Security Practitioner 50%
• Translation (Languages) 50%
• Transformer Model 50%
• Recurrent Layer 50%