SYN flooding attack detection by TCP handshake anomalies
Bibliographic record
Abstract
ABSTRACT We present an original approach to identify synchronize (SYN) flooding attacks from the victim's side, on the basis of a classification of the different forms that TCP handshakes can take during a connection set‐up between a client and a server (e.g. for Web traffic). We first identify the unusual handshake sequences that result from an attack and show how such observations can be used for SYN flooding attack detection. We then introduce a data structure to monitor, in real time, the state of the TCP handshake and study its performance. In addition, we explain the management of the data structure for operations such as initialization, adding and removing flows. Finally, we analyse the effectiveness of our TCP handshake monitoring to identify the presence of SYN flooding attacks by applying it to real traffic traces. To allow quick protection and help guarantee a proper defence, the detection is done in real time. Our detection system uses a non‐parametric cumulative sum algorithm (CUSUM), which has the benefit of not requiring a detailed model of the normal and attack traffic while achieving excellent detection levels. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".