Preventing Unauthorized Messages in DTN Based Mobile Ad Hoc Networks
Bibliographic record
Abstract
Maintaining user connectivity over heterogeneous wireless networks will be a necessity with the wide spread of wireless networks and limited network coverage. In, we propose a super node system architecture based on the concept of delay tolerant networks (DTN) to overcome roaming user intermittent connection over interconnected heterogeneous wireless networks. Mobile ad hoc network plays a key role in the super node system as it can provide a coverage for areas that lack a network infrastructure to bridge the gaps between wireless networks within the system. Long delays combined with the lack of continuous communication with a network manager introduce new security challenges for mobile nodes in a DTN environment. One of the major open challenges is to prevent unauthorized traffic from entering the network. This paper addresses this problem within the super node system. Two schemes are proposed: one is based on asymmetric key cryptography by authenticating a message sender, and the other is based on the idea of separating message authorization checking at intermediate nodes from message sender authentication. Consequently, the second scheme uses symmetric key cryptography in order to reduce the computation overhead imposed on intermediate network nodes, where one-way key chains are used. A simulation study is conducted to demonstrate the effectiveness of each scheme and compare the performance with and without using an authorization scheme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".