Honeypot Networks in Deception Technology for IOT Devices
Bibliographic record
Abstract
In this Digitized world, everyone is looking up for options for which there is no need to go out and perform some specific tasks such as Shopping, transferring money, etc. With all these advantages the problem which has been raised is of Cybercrimes with which everyone is scared of these days. Cybercrimes may occur due to the viruses which can enter into devices using insecure websites and can eventually lead us towards being a victim of cybercrime. In this chapter, the Honeypot Networks and frameworks in deception technology for IoT devices has been discussed. Since IoT has taken over every single device in the market and so the risk associated with them is also higher as compared to any other end device. Some novel and enhanced traps are used to engage the hackers for a longer duration and keeping all the essential data secured. The prime constituents of data captured and data control in honeypot has expatriated and presents a categorization for honeypot as per the targets specified. The technical progress and security contribution of nowadays production and research honeypots will be reviewed. This paper highlights the scenarios of the virtualization and integration of upcoming Honeypots and sees how the fusion of Honeypot networks in Cyber security will be helpful for the better tomorrow.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".