MAIDS for VoIP : a Mobile Agents-based Intrusion Detection System for Voice over Internet Protocol
Bibliographic record
Abstract
Compared to traditional (PSTN) voice networks, a Voice over Internet Protocol network is a convergence of a signaling network and a data network using Internet Protocol (IP). The use of shared media by VoIP systems opens the door to some uncertainty as to the source of a call. While in the traditional voice networks one has to tap into a specific circuit to eavesdrop, in an IP network any equipment connected to the target LAN can identify, store and playback the VoIP packets that traverse that LAN. Unlike traditional voice networks which have only “dumb” end nodes (i.e. simple telephone receivers), VoIP must, by its very nature, deploy intelligent end point devices such as computers andlor IP phones, which are connected to open public networks. An unprotected, unauthenticated IP network makes VoIP susceptible to hostile use, such as call hijacking, connection tear down, denial of service, or sending computer viruses over the network. In this thesis, we perform a series of attacks against a commercial VoIP application, and prove that they succeed with nothing more than a couple of identity tokens captured from the network traffic as prerequisites. We then leverage the mobile agent-based framework introduced by APHIDS to design an Intrusion Detection System implementing a gradual attack-response procedure, destined to inform and protect the End-Users of the Application Under Test when specific, internet telephony attacks do occur, and ultimately to block the capability of the attack perpetrator to induce further damage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".