An approach towards anomaly based detection and profiling covert TCP/IP channels
Bibliographic record
Abstract
Firewalls and detection systems have been used for preventing and detecting attacks by a wide variety of mechanisms. A problem has arisen where users and applications can circumvent security policies because of the particularities in the TCP/IP protocol, the ability to obfuscate the data payload, tunnel protocols, and covertly simulate a permitted communication. It has been shown that unusual traffic patterns may lead to discovery of covert channels. Presently, we are not aware of any schemes that address detecting anomalous traffic patterns that can potentially be created by a covert channel. In this work, we will explore the approach of combining anomaly based detection and covert channel profiling to be used for detecting a very precise subset of covert storage channels in network protocols. We shall also discuss why this method is more practical and industry-ready compared to the present research on how to profile and mitigate these types of attacks. Finally, we shall describe a specialized tool to passively monitor networks for these types of attacks and show how it can be used to build an efficient hybrid covert channel and anomaly based detection system.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".