A Survey on Some Currently Existing Intrusion Detection Systems for Mobile Ad Hoc Networks
Bibliographic record
Abstract
Mobile Ad-Hoc Network (MANET) is one of the most promising technologies that have applications in military, environmental, space exploration and forestry industry areas. This type of network has attractive features such as its low transmission power to conserve energy, increase throughput, and reduce delay. However, it suffers from many constraints, including limited resources, and the use of insecure wireless communication channels. Due to the lack of defense, the security of these networks is a worthy concern, particularly for the applications where confidentiality has prime importance. Thus, any kind of intrusions should be detected before attackers can harm the network in order to operate MANET in a secure way. In this article, we present a survey of the state-of-theart in Intrusion Detection Systems (IDSs) that are proposed for MANETs. This is followed by a comparison of each scheme along with their advantages and disadvantages. This survey is concluded by highlighting open research issues in the field.
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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 | 0.000 | 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".