Bibliographic record
Abstract
Network access control (NAC) systems have a very important role in network security. However, \nNAC policy configuration is an extremely complicated and error-prone task due to the semantic \ncomplexity of NAC policies and the large number of rules that could exist. This significantly \nincreases the possibility of policy misconfigurations and network vulnerabilities. NAC policy \nmisconfigurations jeopardize network security and can result in a severe consequence such as \nreachability and denial of service problems. In this thesis, we choose to study and analyze the NAC \npolicy configuration of two significant network security devices, namely, firewall and IDS/IPS. \nIn the first part of the thesis, a visualization technique is proposed to visualize firewall rules and \npolicies to efficiently enhance the understanding and inspection of firewall configuration. This is \nimplemented in a tool called PolicyVis. Our tool helps the user to answer general questions such as \n‘‘Does this policy satisfy my connection/security requirements’’. If not, the user can detect all \nmisconfigurations in the firewall policy. \nIn the second part of the thesis, we study various policy misconfigurations of Snort, a very popular \nIDS/IPS. We focus on the misconfigurations of the flowbits option which is one of the most important \nfeatures to offers a stateful signature-based NIDS. We particularly concentrate on a class of flowbits \nmisconfiguration that makes Snort susceptible to false negatives. We propose a method to detect the \nflowbits misconfiguration, suggest practical solutions with controllable false positives to fix the \nmisconfiguration and formally prove that the solutions are complete and sound.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".