Detecting Flood-based Attacks against SIP Proxy Servers and Clients using Engineered Feature Sets
Bibliographic record
Abstract
Session Initiation Protocol (SIP) is the main signaling protocol of the next generation networks. The security issues of SIP-based entities (i.e. proxy servers and clients) have a direct impact on the perceived quality of experience of end users in multimedia sessions. In this paper, our focus is on the S IP flooding attacks including denial of service and distributed denial of service attacks. After classifying various types of SIP attacks based on their sources, we extract four feature sets based on the specification of its attack group, as well as the normal behavior of the SIP state machine specified in RFC 3261. We then minimize the number of derived features in each set to reduce the computational complexity of our proposed approach. This facilitates employing the engineered feature sets in embedded S IP-based devices such as cell phones and smart TVs. We evaluate the performance of the proposed feature sets in detecting SIP attack sequence. For this, we design and implement a real test-bed for SIP-based services to generate normal and attack traffics. The experimental results confirm that the engineered feature sets perform well in terms of detection accuracy and false alarm rates in classifying benign and anomaly traffic in various attack scenarios.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".