NetADHICT: a tool for understanding network traffic
Bibliographic record
Abstract
Computer and network administrators are often confused or uncertain about the behavior of their networks. Traditional analysis using IP ports, addresses, and protocols are insufficient to understand modern computer networks. Here we describe NetADHICT, a tool for better understanding the behavior of network traffic. The key innovation of NetADHICT is that it can identify and present a hierarchical decomposition of traffic that is based upon the learned structure of both packet headers and payloads. In particular, it decomposes traffic without the use of protocol dissectors or other application-specific knowledge. Through an AJAX-based web interface, NetADHICT allows administrators to see the high-level structure of network traffic, monitor how traffic within that structure changes over time, and analyze the significance of those changes. NetADHICT allows administrators to observe global patterns of behavior and then focus on the specific packets associated with that behavior, acting as a bridge from higher level tools to the lower level ones. From experiments we believe that NetADHICT can assist in the identification of flash crowds, rapidly propagating worms, and P2P applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".