Intrusion detection in IoT and wireless networks using image-based neural network classification

Keyphrases

• Benchmark Dataset 33%
• Bidirectional Long Short-Term Memory (BiLSTM) 33%
• Complex Networks 33%
• Cybersecurity 66%
• Data Processing Methods 33%
• Dataset Benchmark 33%
• Deep Convolutional Neural Network (deep CNN) 33%
• Deep Learning Model 33%
• Deep Learning Network 33%
• Detection-based 33%
• Fifth Generation 5G 33%
• Highly Effective 33%
• Image Classification Neural Networks 33%
• Image Processing 33%
• Image-based 100%
• Internet of Things Networks 100%
• Internet of Vehicles 66%
• Intrusion Detection 100%
• Intrusion Detection System 66%
• LeNet 33%
• Machine Learning Algorithms 33%
• Malicious Behavior 33%
• Malicious Security 33%
• Modern History 33%
• Modified LeNet 33%
• Multi-class Classification 33%
• Network Characteristics 33%
• Network Traffic 33%
• Network Traffic Patterns 33%
• Network-based Intrusion Detection 33%
• Neural Network Classifier 100%
• Neural Network-based 33%
• NSL-KDD 66%
• One-dimensional CNN 33%
• Performance Improvement 33%
• Processing Image 33%
• Processing Network 33%
• Security Threats 66%
• Structure Modification 33%
• Telecommunication Network 33%
• Test Accuracy 33%
• Wired Network 66%
• Wireless Networks 100%

Computer Science

• Bidirectional Long Short-Term Memory Network 25%
• Complex Networks 25%
• Convolutional Neural Network 25%
• Cybersecurity 50%
• Data Preprocessing 25%
• Deep Convolutional Neural Networks 25%
• Deep Learning Method 25%
• Deep Learning Model 25%
• Experimental Result 25%
• Extracted Feature 25%
• Image Classification 25%
• Image Processing 50%
• Internet of Vehicles 50%
• Internet-Of-Things 100%
• Intrusion Detection 100%
• Intrusion Detection System 50%
• Machine Learning Algorithm 25%
• Multiclass Classification 25%
• Network Traffic 50%
• Neural Network 100%
• Structured Format 25%
• Telecommunication Network 25%
• Traffic Pattern 25%
• Video Processing 25%
• Wired Network 50%
• Wireless Network 100%