Temporal Representations for Detecting BGP Blackjack Attacks
Bibliographic record
Abstract
Even though BGP blackholes are used to mitigate denial of service attacks, they also represent a major cybersecurity challenge to organizations. These challenges include abuse of route selection algorithms, lack of host verification, and maliciously triggering a blackhole, i.e. BGP blackjack. This research presents a supervised machine learning based approach for blackjack detection. We employ Naive Bayes and Decision Tree classifiers with three different temporal representations: (i) packets with/without timestamps; (ii) buffer of packets with/without timestamps; and (iii) overlapping / non-overlapping buffer of packets with/without timestamps. Our goal is to understand the effect of temporal data and context in the detection of blackjack attacks. Furthermore, we explore the most suitable attributes and solution complexity. Evaluations show that using overlapping buffer data with times-tamps achieves the highest accuracy/recall using five of the seven BGP attributes. We also observe that high performance is not correlated with complex solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".