Unknown, Atypical and Polymorphic Network Intrusion Detection: A Systematic Survey
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
Agile network security is paramount in our modern world which is currently dominated by Internet systems and expanding digital spaces. This rapid digital transformation has created more opportunities for cyberattackers to exploit different vulnerabilities and launch sophisticated and continuously evolving cyberattacks. Increasingly, intrusion detection systems are relying on new methods based on Machine Learning (ML) and Deep Learning (DL) techniques to detect and mitigate such cyberattacks. While such techniques normally can identify known network attack patterns with a reasonable degree of success, their ability to identify complicated atypical, polymorphic, and unknown attacks is shown to be limited. In this paper, we present a comprehensive survey of recent research for detecting unknown, atypical, and polymorphic network attacks using DL techniques. We further highlight and discuss the main challenges in this area and identify the future research directions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 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