EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
Bibliographic record
Abstract
The presence of malevolent nodes in mobile ad hoc networks (MANETs) would lead to genuine security concerns. These nodes may disturb the routing process or deform the pattern of the data packets passing through the network. The MANET is extremely liable to attacks, owing to its characteristics of the network framework, such as the absence of infrastructure, moveable topology, and a centralized control unit. One of the most common attacks in MANETs is the blackhole attack. MANET nodes are susceptible to spectacular degradation of network performance in the presence of such attacks. In this regard, detecting or preventing deceitful nodes that will launch blackhole attacks is a challenge in MANETs. In this paper, we propose an Enhanced Blackhole Resistance (EBR) protocol to identify and resist nodes that are responsible for blackhole attacks. EBR can avoid congested traffic by passing the data packets through a safe route with the minimum RTT. The EBR protocol uses a combination of time to live (TTL) and round trip time (RTT), which is also called a TR mechanism, to detect the blackhole attacks. Our algorithm does not require any cryptographic or authentication mechanisms. Simulation results prove that EBR behaves better than other protocols in terms of throughput, end-to-end delay, packet delivery ratio, energy, and routing overhead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".