Preventing Collaborative Blackhole Attacks on Mobile Ad Hoc Networks
Bibliographic record
Abstract
This thesis proposes two protocols for addressing collaborative blackhole attacks in MANETs, referred to as the Detecting Blackhole Attack-Dynamic Source Routing(DBA-DSR) and Detecting Collaborative Blackhole Attack (DCBA) algorithms. The DBA-DSR protocol uses fake Route request packets to attract the malicious nodes before the actual routing process. The DCBA protocol uses our so-called suspicious value, which is based on the abnormal difference between the routing messages transmitted through a node, to identify the malicious nodes. In later stage, if the destination node detects significant loss in data packets, the initial detecting mechanism will be triggered again to identify malicious nodes. Simulation results are provided, showing significant improvement over the DSR protocol, as well as the Baited blackhole DSR protocol(chosen as a benchmark scheme), in terms of performance metrics such as packet delivery ratio, network throughput, average-end-to-end delay and routing overhead.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".