Adaptabilty of a GP Based IDS on Wireless Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract—Security and Intrusion detection in WiFi networks is currently an active area of research where WiFi specific Data Link layer attacks are an area of focus; particularly recent work has focused on producing machine learning based IDSs for these WiFi specific attacks. These proposed machine learning based IDSs come in addition to the already deployed signatures which are already in use in conventional intrusion detection systems like Snort-Wireless and Kismet. In this paper, we compare the detection capability of Snort-Wireless and a Genetic Programming (GP) based intrusion detector, based on the ability to adapt to modified attacks, ability to adapt to similar unknown attacks and infrastructure independent detection. Our results show that the GP based detection system is much more robust against modified attacks compared to Snort-Wireless. Moreover, by focusing on the method(s) used in feature preprocessing for presentation to learning algorithms, GP based IDSs can achieve infrastructure independent detection and can adapt to similar unknown attacks too. On the other hand, even though Snort-Wireless is an infrastructure independent detector, it cannot adapt to unknown attacks even if they are similar to others for which it has signatures on.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it