Security vulnerabilities and countermeasures against jamming attacks in Wireless Sensor Networks: A survey
Bibliographic record
Abstract
Wireless sensor network is a collection of several nodes in a network capable of sharing information in wireless medium. Due to the presence of wireless medium it is highly vulnerable to network attacks. WSN has many applications ranging from monitoring of environment to highly restricted military and surveillance where security is a major requirement. The most common and dangerous attack which can be proved harmful for WSN is jamming attack. In jamming an adversary can limit the capabilities of WSN communication by interfering with RF signals using certain jamming devices. Securing a WSN is one of the major concerns of network safety. In this paper, we have discussed various jamming techniques and types of jammers. We have also listed some countermeasures of jamming which, if used, depending on the application can significantly reduce the chances and effects of jamming. Some limitations and challenges of WSN are also explained. Starting from the introduction about the WSN, our work will cover all the pros and cons of the jammers and countermeasures which can help an interested researcher to gain some knowledge about the topic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".