Detecting Forged Acknowledgements in MANETs
Bibliographic record
Abstract
Over the past few years, with the trend of mobile computing, Mobile Ad hoc Network (MANET) has become one of the most important wireless communication mechanisms among all. Unlike traditional network, MANET does not have a fixed infrastructure, every single node in the network works as both a receiver and a transmitter. Nodes directly communicate with each other when they are both within their communication ranges. Otherwise, they rely on their neighbors to store and forward packets. As MANET does not require any fixed infrastructure and it is capable of self configuring, these unique characteristics made MANET ideal to be deployed in a remote or mission critical area like military use or remote exploration. However, the open medium and wide distribution of nodes in MANET leave it vulnerable to various means of attacks. It is crucial to develop suitable intrusion detection scheme to protect MANET from malicious attackers. In our previous research, we have proposed a mechanism called Enhanced Adaptive Acknowledgement (EAACK) scheme. Nevertheless, it suffers from the threat that it fails to detect misbehaving node when the attackers are smart enough to forge the acknowledgement packets. In this paper, we introduce Digital Signature Algorithm (DSA) into the EAACK scheme, and investigate the performance of DSA in MANET. The purpose of this paper is to present an improved version of EAACK called EAACK2 that performs better in the presence of false misbehavior and partial dropping.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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 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".