Protecting Routing Data in WSNs with use of IOTA Tangle
Bibliographic record
Abstract
Routing in wireless sensor networks (WSNs) are based on multi-hop communication in which the messages pass through multiple sensor nodes, and hence routing algorithms must rely on trust relationships between neighboring nodes. The open access nature of WSNs leads to the possibility of nodes becoming compromised and consequently being turned into malicious objects. One such attack on WSNs is the Sybil attack, in which an attacker can take control of a legitimate node or enter a malicious node into the network and create fake identities. Consequently, they can change the behavior of the WSN, such as its routing schema to cause loops or wrong directions to manipulate data and consume the energy of the network, or even target cluster heads. In this paper, we present a novel technique based on IOTA Tangle, a distributed ledger technology, for the detection and prevention of Sybil attacks by protecting routing data. A transaction history on IOTA is maintained for detecting malicious node injection, and IOTA currency is used as a reputation score to prevent malicious nodes and protect the routing table. Even if an attacker gains access to the network, all routing data can be tracked in IOTA Tangle that will alert the base station about this attack. The technique has been simulated and evaluated using a proof-of-concept prototype.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".