Comparison of two security protocols for preventing packet dropping and message tampering attacks on AODV-based mobile ad Hoc networks
Bibliographic record
Abstract
In Emergency MANETs (eMANETs), the broadcasting nature of the wireless medium, the lack of pre-established trust relationship among nodes, and the frequent topology changes, cause some serious security challenges, making the network vulnerable to malicious attacks such as wormhole attacks. This paper investigates a recently proposed Advanced Encryption Standard (AES)-based routing algorithm (so-called AODV-Wormhole Attack Detection Reaction - here referred to as AODV-WADR-AES) for securing AODV-based eMANETs against wormhole attacks. The proposal consists of substituting the AES part of the scheme by the Triple Data Encryption Standard (TDES), yielding the AODV-WADR-TDES routing algorithm, with the goal to study the performance of the algorithm where mobile devices that are incompatible with AES are part of eMANET nodes. In doing so, markers in the form of hash codes are included in the data packets to help consolidating the data integrity. Simulation results are presented to validate the proposed AODV-WADR-TDEA scheme. It is also shown that the AODV-WADR-AES scheme outperforms the AODV-WADR-TDES scheme in terms of end-to-end delay, packet delivery ratio, and number of packets traversing through the wormhole link.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".