An efficient collusion resistant security mechanism for heterogeneous sensor networks
Bibliographic record
Abstract
Purpose As large‐scale homogeneous networks suffer from high costs of communication, computation, and storage requirements, the heterogeneous sensor networks (HSN) are preferred because they provide better performance and security solutions for scalable applications in dynamic environments. Random key pre‐distribution schemes are vulnerable to collusion attacks. The purpose of this paper is to propose an efficient collusion resistant security mechanism for heterogeneous sensor networks. Design/methodology/approach The authors consider a heterogeneous sensor network (HSN) consists of a small number of powerful high‐end H‐sensors and a large number of ordinary low‐end L‐sensors. However, homogeneous sensor network (MSN) consists of only L‐sensors. Since the collusion attack on key pre‐distribution scheme mainly takes advantage of the globally applicable keys, which are selected from the same key pool, they update the key ring after initial deployment and generate new key rings by using one‐way hash function on nodes' IDs and initial key rings. Further, in the proposed scheme, every node is authenticated by the BS in order to join the network. Findings The analysis of the proposed scheme shows that even if a large number of nodes are compromised, an adversary can only exploit a small number of keys near the compromised nodes, while other keys in the network remain safe. Originality/value The proposed key management scheme described in the paper outperforms the previous random key pre‐distribution schemes by: considerably reducing the storage requirement, and providing more resiliency against node capture and collusion attacks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".