Detecting Wormhole Attacks in Mobile Ad Hoc Networks through Protocol Breaking and Packet Timing Analysis
Bibliographic record
Abstract
We have implemented a fully-functional wormhole attack in an IPv6 802.11b wireless mobile ad hoc network (MANET) test bed running a proactive routing protocol. Using customised analysis tools we study the traffic collected from the MANET at three different stages: i) regular operation, ii) with a "benign" wormhole joining distant parts of the network, and iii) under stress from wormhole attackers who control a link in the MANET and drop packets at random. Our focus is on detecting anomalous behaviour using timing analysis of routing traffic within the network. We first show how to identify intruders based on the protocol irregularities that their presence creates once they begin to drop traffic. More significantly, we go on to demonstrate that the mere existence of the wormhole itself can be identified, before the intruders begin the packet-dropping phase of the attack, by applying simple signal-processing techniques to the arrival times of the routing management traffic. This is done by relying on a property of proactive routing protocols- that the stations must exchange management information on a specified, periodic basis. This exchange creates identifiable traffic patterns and an intrinsic "valid station" fingerprint that can be used for intrusion detection
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".