A cryptography‐based protocol against packet dropping and message tampering attacks on mobile ad hoc networks
Bibliographic record
Abstract
ABSTRACT In mobile ad hoc networks (MANETs), nodes are mobile in nature, but at the same time, they are assumed to rely on each other to relay their traffic even in case the wireless transmission medium is out of range. This requirement poses a serious challenge when malicious nodes are present in the MANET and may contribute to the routing operations, either by tampering the data packets or dropping them. This paper addresses this particular type of wormhole attacks, by introducing an enhancement (the so‐called E‐HSAM) to a recently proposed ad hoc on‐demand distance vector‐based protocol for preventing against such attacks in MANETs (the so‐called highly secured approach against attacks on MANETs (HSAM)). Our contributions are twofold: (i) a simulation study of the HSAM protocol is provided for the first time, and (ii) the Advanced Encryption Standard (AES) is introduced in the route selection phase of E‐HSAM (yielding our so‐called E‐HSAM‐AES scheme) to strengthen the integrity of the data while securing the potential routes chosen for data transfer from source to destination nodes. Simulation results are presented, showing the superiority of E‐HSAM‐AES over E‐HSAM and HSAM in terms of packet delivery ratio and broken link detected during data transmission, chosen as performance metrics. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".