Enhanced Intrusion Detection System for Discovering Malicious Nodes in Mobile Ad Hoc Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As mobile wireless ad hoc networks have different characteristics from wired networks and even from standard wireless networks, there are new challenges related to security issues that need to be addressed. Many intrusion detection systems have been proposed and most of them are tightly related to routing protocols, such as Watchdog/Pathrater and Routeguard. These solutions include two parts: intrusion detection (Watchdog) and response (Pathrater and Routeguard). Watchdog resides in each node and is based on overhearing. Through overhearing, each node can detect the malicious action of its neighbors and report other nodes. However, if the node that is overhearing and reporting itself is malicious, then it can cause serious impact on network performance. In this paper, we overcome the weakness of Watchdog and introduce our intrusion detection system called ExWatchdog. The main feature of the proposed system is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation results show that our system decrease the overhead greatly, though it does not increase the throughput obviously.
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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 | 0.001 |
| 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 it