Detecting blackhole attacks on DSR-based mobile ad hoc networks
Bibliographic record
Abstract
Mobile ad hoc network (MANET) is a collection of mobile nodes that communicate with each other without any fixed infrastructure or a central network authority. From a security design perspective, MANETs have no clear line of defense; i.e. no built-in security. Thus, the wireless channel is accessible to both legitimate network users and malicious attackers. A blackhole attack is a severe attack that can be easily employed against data routing in MANETs. A blackhole is a malicious node that can falsely reply for any route requests without having an active route to a specified destination and drops all the receiving data packets. In this paper, a novel scheme for Detecting Blackhole Attacks in MANETs (so-called DBA-DSR) is introduced. The BDA-DSR protocol detects and avoids the blackhole problem before the actual routing mechanism is started by using fake RREQ packets to catch the malicious nodes. Simulation results are provided, showing that the proposed DBA-DSR scheme outperforms DSR in terms of packet delivery ratio and network throughput, chosen as performance metrics, when blackhole nodes are present in the network.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".