Efficient BGP Intrusion Detection Model Using Machine Learning: A Comparative Study with AdaBoost as the Optimal Classifier
Bibliographic record
Abstract
The Border Gateway Protocol (BGP) is a crucial component of the Internet's infrastructure that enables the exchange of routing information among multiple Autonomous Systems so data flow from one network to another becomes possible. However, rare anomalies in BGP, such as IP prefix hijacks, misconfigurations, and worm attacks, when they occur, can cause significant disruptions to the network and threaten the stability and reliability of the Internet. Considerable efforts have been made to understand the nature of normal and abnormal BGP updates to identify and mitigate their disruptive consequences. Recent studies in the literature suggest that machine learning (ML) techniques can achieve a high level of accuracy and robustness in anomaly detection. To fully leverage the advantages of ML techniques, it is necessary to pre-process the data and choose a suitable model that helps identify and mitigate against any such BGP anomalies and improve the stability and reliability of the Internet. This paper evaluates multiple machine learning models for detecting BGP anomalies and comprehensively analyzes their effectiveness. Results reveal that AdaBoost achieves an impressive accuracy of 97.22%, making it the optimal choice for BGP anomaly detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".