Spark-Based Network Security Honeypot System: Detailed Performance Analysis
Bibliographic record
Abstract
In the contemporary world, network security has been of the biggest importance and acute worry in both individual and institutional wisdom, concurrent with the newly emerging technologies. Firewalls, encryption techniques, intrusion detection systems, and honeypots are just a few of the systems and technologies that have been developed to ensure information security. Systems for safeguarding an organizational environment through various defensive strategies are traditionally developed. "The enemy continues on attacking" is a primarily defensive statement. By empowering an organization to take action, Honeypot demonstrates its importance. An institution can discover, gather, address, and absorb new security policy flaws with the aid of honeypots. This methodology allows an organization's security measures to continuously incorporate new threats and penetration methods. This is the main justification of creating, developing, and using a honeypot. It is a resource that is meant to be taken advantage of and compromised. To evaluate the methodology, a spark-based honeypot strategy has been designed, put into practice, and tested in this study. The suggested study strategy has undergone many days of testing on a campus-based network. The designed system's primary function is to behave as a fully resourced computer or vault to draw intruders with the requirement that they not defend against or respond to intrusions. The studies were carried out using a number of parameters that were designed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".