* The reliance on `pyshark` summaries limits critical feature engineering required for robust network security and anomaly detection. * Refactor multi-label target creation using vectorized NumPy or Pandas operations to improve data preprocessing throughput significantly. * The current device identification function is a deterministic placeholder; replace IP hashing with actual network fingerprinting heuristics for real-world utility. * The architecture successfully separates PCAP extraction logic from the TensorFlow/Keras deep learning modeling components. * Couple the analyzer and model execution via direct function calls, eliminating inefficient intermediate CSV file I/O for production pipelines. * Address the reported critical security issues by implementing more complex feature extraction beyond basic packet length and port details.
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