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 ...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.展开更多
文摘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.
