Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments

Traditional single-machine Network Intrusion Detection Systems (NIDS) are increasingly challenged by rapid network traffic growth and the complexities of advanced neural network methodologies. To address these issues, we propose an Enhanced Meta-IDS framework inspired by meta-computing principles, enabling dynamic resource allocation for optimized NIDS performance. Our hierarchical architecture employs a three-stage approach with iterative feedback mechanisms. We leverage these intervals in real-world scenarios with intermittent data batches to enhance our models. Outputs from the third stage provide labeled samples back to the first and second stages, allowing retraining and fine-tuning based on the most recent results without incurring additional latency. By dynamically adjusting model parameters and decision boundaries, our system optimizes responses to real-time data, effectively balancing computational efficiency and detection accuracy. By ensuring that only the most suspicious data points undergo intensive analysis, our multi-stage framework optimizes computational resource usage. Experiments on benchmark datasets demonstrate that our Enhanced Meta-IDS improves detection accuracy and reduces computational load or CPU time, ensuring robust performance in high-traffic environments. This adaptable approach offers an effective solution to modern network security challenges.