A modular security architecture for managing security associations in MANETs
Bibliographic record
Abstract
Maintaining security associations (SA) in mobile ad hoc networks (MANET) is challenging due to their intrinsically open, dynamic, and decentralized nature. Bandwidth limitations arising from both the physical characteristics of the wireless medium and the control overhead required to maintain routes in a network with changing topology add another level of difficulty to the problem. While establishing SAs with strong authentication is a generally accepted practice, the allowed duration of these SAs is a harder problem that may depend on a number of factors. Ideally, we would like to optimize the maintenance of the SAs to balance quality of protection (QoP) against quality of service (QoS). In this paper we propose and describe a modular security architecture to achieve this goal. The architecture consists of security policy, trust model, and state machine modules that together control the strong authentication process for establishing and maintaining SAs. We demonstrate the efficacy of this architecture through simulation of a MANET that implements a Trust-enhanced Routing Table (TRT). Our simulations use a state machine to manage the authentication process linked to a TRT previously proposed as a security extension of the optimized link state routing (OLSR) protocol. We demonstrate that this state machine, when linked to an adaptive trust model itself controlled by a security policy, can substantially outperform static models. Because the architecture is modular, the implementation can be tailored for different environments or scenarios.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".