Reputation Management Using Honeypots for Intrusion Detection in the Internet of Things
Bibliographic record
Abstract
Internet of Things (IoT) networks consist of tiny devices with limited processing resources and restricted energy budget. These devices are connected to the world-wide web (www) using networking protocols. Considering their resource limitations, they are vulnerable to security attacks by numerous entities on the Internet. The classical security solutions cannot be directly implemented on top of these devices for this reason. However, an Intrusion Detection System (IDS) is a classical way to protect these devices by using low-cost solutions. IDS monitors the network by introducing various metrics, and potential intruders are identified, which are quarantined by the firewall. One such metric is reputation management, which monitors the behavior of the IoT networks. However, this technique may still result in detection error that can be optimized by combining this solution with honeypots. Therefore, our aim is to add some honeypots in the network by distributing them homogeneously as well as randomly. These honeypots will team up with possible maliciously behaving nodes and will monitor their behavior. As per the simulation results, this technique reduces the error rate within the existing IDS for the IoT; however, it costs some extra energy. This trade-off between energy consumption and detection accuracy is studied by considering standard routing and MAC protocol for the IoT network.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".