Feature engineering for detection of <scp>Denial of Service</scp> attacks in session initiation protocol
Bibliographic record
Abstract
Abstract The Session Initiation Protocol (SIP) is a text‐based protocol, which defines the messaging between the SIP entities to establish, maintain, and terminate a multimedia session. Because of the text‐ and transaction‐based nature of the SIP protocol, it encounters various types of malformed message and resource depletion attacks. In this paper, we study the security concerns of the SIP‐based systems, and propose a feature set for it. Engineered features are derived from the SIP header fields in real time detecting the deviation of the input traffic from normal state. These features are built at three levels: packet, transaction, and dialog. The designed features can accurately detect the SIP known attacks. Moreover, because we successfully model the state machine of SIP during its normal behavior, we can also identify the unknown attacks. To study the effectiveness of the engineered feature set, we employ them in a sample one‐class support vector machine classifier. We evaluate the engineered features on three different datasets with various types of attack scenarios including resource depletion and authentication and brute force attacks. The impact of these attack scenarios on the designed features are shown in different test cases to demonstrate the effectiveness of our proposed feature set. Copyright © 2014 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 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".