Detection method against SYN Flooding attacks based on source end by analysis of time series
Bibliographic record
Abstract
This paper proposed a method of detecting DDoS attacks based on source end by analyzing the abrupt change of time series data.By detecting and predicting the data flow in the Internet at source end,the method could judge whether SYN Flooding was occurred or not for providing the foundation for the victim end.It extracted the characteristic information of data flow by using the self-similarity of network traffic flow and Bloom Filter algorithm,so that it could construct the time series of the network traffic flow and build the auto-regressive(AR) forecasting model.By dynamically forecasting traffic flow and comparing with definite threshold,pre-alert was sent and response was ahead adopted.The experimental results show that the scheme can count the number of the data packages and the number of the new IP data packages with the better detection rate and lower misinformation rate,besides,it can predict the traffic flow in the next period even several periods correctly,which can provide strong support for effectively defending against SYN Flooding attacks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".