Bibliographic record
Abstract
The Session Initiation Protocol (SIP) implements methods for generic service discovery and versatile messaging. It is, therefore, expected to be a key component in many telecommunication and Internet services. For example, the 3GPP IP Multimedia Subsystem relies heavily on SIP. Given its critical role, ensuring the security of SIP is clearly a crucial task. In this paper, we analyze the SIP protocol and show that it can easily be exploited to mount effective and large-scale botnets. We do this by scrutinizing the details of the SIP protocol and show how it offers a variety of ways to conceal botnet traffic within legitimate-looking SIP traffic. Using our analysis, we implement a SIP bot and present experimental results from a real testbed network. In addition, we employ traffic statistics collected from a large telecommunication provider and discuss the implications for both botnet design and detection. Finally, we present a software tool (called autosip) to generate synthetic traffic that resembles actual SIP traffic with different controllable characteristics. The proposed tool is quite useful for researchers working in the area who may not have access to traffic dumps from actual telecommunication providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".