Implementation of A3ACKs Intrusion Detection System under Various Mobility Speeds
Bibliographic record
Abstract
Wireless networking is an emerging technology that allows users to access information and services anywhere regardless of their geographic location. Mobile Ad hoc Network (MANETs) is one of the most significant technologies among various wireless communication technologies. In MANETs, all nodes are mobile and can be connected dynamically using wireless link in a random manner. All nodes work as routers and take part in discovery and maintenance of routes to other nodes in the network. MANETs are unique infrastructure less network and have self-configuring features make them suitable for many critical applications, such as military and emergency applications. However, these features make them also vulnerable for all types of passive and active attacks because of open environment, the rapidly changing topology and the decentralization of nodes. In addition, most of the proposed protocols assume that all nodes in the network are cooperative, and do not address security issues. Moreover, most of the proposed existing intrusion detection systems (IDSs) of are based on Watchdog technique. In this paper, we propose and implement a new intrusion detection system named Adaptive three ACKnowledgments (A3ACKs) that solves three significant problems of Watchdog technique, mainly: receiver collision, limited transmission power and collaborative attacks. We use Network Simulator 2 (NS2) to implement and test our proposed system under different networks with various mobility speeds as well as compare our results with the results of some closely existing IDSs mechanism.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".