A Naive Bayes-Driven Mechanism for Mitigating Packet-Dropping Attacks in Autonomous Wireless Networks
Bibliographic record
Abstract
Autonomous wireless networks, characterized by peer-to-peer connectivity and dynamic topology, enable efficient internet access, irrespective of geographical constraints.Their applications span across disaster relief, military operations, road safety, and healthcare, areas where secure communication is indispensable.This paper addresses the crucial challenge of packet-dropping attacks in these networks, a security issue that has not yet been thoroughly explored in current literature.Conventional mechanisms for preventing packet-dropping often fail to differentiate between malicious attacks and system faults, underscoring the need for an effective classification system.Such a system should discern whether packet-dropping incidents are a result of malevolent attacks or system faults, and appropriately penalize only the malicious nodes.In this light, we introduce an Intrusion Detection System (IDS) based on the Naive Bayes algorithm to mitigate packet-dropping attacks in autonomous wireless networks.This algorithm has demonstrated efficacy in distinguishing between different categories based on statistical probabilities.By successfully mitigating both malicious attacks and system faults, our proposed IDS significantly enhances network performance.Simulation results confirm the effectiveness of the proposed IDS, showing notable improvements in packet delivery, delay, and energy efficiency.This IDS, therefore, not only detects and eliminates packet-dropping nodes that disrupt network operations but also extends the overall network performance.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".