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, e...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | High-Confidence Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295225000029 |
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| _version_ | 1839610863835676672 |
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| author | Nadia Niknami Vahid Mahzoon Slobadan Vucetic Jie Wu |
| author_facet | Nadia Niknami Vahid Mahzoon Slobadan Vucetic Jie Wu |
| author_sort | Nadia Niknami |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1ea0dda3a94e4ccb9ebbc3d1d143e8de |
| institution | Matheson Library |
| issn | 2667-2952 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | High-Confidence Computing |
| spelling | doaj-art-1ea0dda3a94e4ccb9ebbc3d1d143e8de2025-07-29T04:12:48ZengElsevierHigh-Confidence Computing2667-29522025-09-0153100298Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustmentsNadia Niknami0Vahid Mahzoon1Slobadan Vucetic2Jie Wu3Center for Networked Computing, Temple University, Philadelphia 19122, USA; Corresponding author.Center for Hybrid Intelligence, Temple University, Philadelphia 19122, USACenter for Hybrid Intelligence, Temple University, Philadelphia 19122, USACenter for Networked Computing, Temple University, Philadelphia 19122, USATraditional 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.http://www.sciencedirect.com/science/article/pii/S2667295225000029Adaptive IDSCPU timeDynamic adaptationIntrusion detection system(IDS)Meta-computing |
| spellingShingle | Nadia Niknami Vahid Mahzoon Slobadan Vucetic Jie Wu Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments High-Confidence Computing Adaptive IDS CPU time Dynamic adaptation Intrusion detection system(IDS) Meta-computing |
| title | Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments |
| title_full | Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments |
| title_fullStr | Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments |
| title_full_unstemmed | Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments |
| title_short | Enhanced Meta-IDS: Adaptive multi-stage IDS with sequential model adjustments |
| title_sort | enhanced meta ids adaptive multi stage ids with sequential model adjustments |
| topic | Adaptive IDS CPU time Dynamic adaptation Intrusion detection system(IDS) Meta-computing |
| url | http://www.sciencedirect.com/science/article/pii/S2667295225000029 |
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