Concerning Enterprise Network Vulnerability to HTTP Tunnelling
Bibliographic record
Abstract
It has been understood for some time that arbitrary data, including the communications associated with malicious backdoors and Trojan horses, can be tunnelled by subverting the HTTP protocol. Although there are a number of demonstration programs openly available, the risks associated with this vulnerability have not been characterised in the literature. This research investigates the nature of the vulnerability and the efficacy of contemporary network defence strategies such as firewall technology, intrusion detection systems, HTTP caching and proxying, and network address translation. All of these techniques are quite easily circumvented by HTTP tunnelling strategies. This vulnerability is serious for most enterprise environments today. The use of some Internet services is considered to be a requirement for business operations in many organisations. Even with very strict firewall rule sets and layered defence architectures, legitimate web traffic originating from within the protected network is often allowed. Web traffic also forms a large portion of the traffic crossing network boundaries, which makes the HTTP protocol an attractive target for subversion. This research explores techniques that may be used to hide malicious traffic in what seems to he legitimate HTTP traffic originating from within the protected network. The covert channel provides external control of a computer on the protected network from a machine anywhere on the Internet. The techniques explored by this project are used in parallel research projects to detect such malicious tunnel traffic and validate new intrusion detection technology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".