How effective are transformer-based models (e.g., BERT or LLaMA) compared to traditional CNN/RNN architectures in intrusion detection for high-speed networks?
Transformer-based models like BERT and LLaMA are gaining traction in network intrusion detection, especially for high-speed environments, due to their ability to capture long-range dependencies and complex patterns across sequential data. Compared to traditional CNNs or RNNs, transformers offer enhanced context awareness and parallel processing—key for real-time packet inspection and anomaly detection at scale.
In my research paper titled “FUZZY-OPTIMIZED LIGHTWEIGHT CYBER-ATTACK DETECTION FOR SECURE EDGE-BASED IOT NETWORKS,” we explored lightweight yet adaptive ML models tailored for edge-based intrusion detection. While we primarily focused on fuzzy-optimized classical models for constrained environments, the findings highlighted the need for more contextual models like transformers in high-throughput systems.
Transformers integrated with attention-aware feature selection or hybridized with lightweight CNN layers can balance interpretability and performance in intrusion detection for modern networks. As threats evolve rapidly, combining transformer efficiency with edge adaptability may lead to more resilient IDS architectures.
FUZZY-OPTIMIZED LIGHTWEIGHT CYBER-ATTACK DETECTION FOR SECURE EDGE-BASED IOT NETWORKS :- Article FUZZY-OPTIMIZED LIGHTWEIGHT CYBER-ATTACK DETECTION FOR SECUR...
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