Multiscale Hebbian neural network for cyber threat detection
Bibliographic record
Abstract
The recent blaze in cyber espionage has posed unprecedented challenges to the cutting edge network intrusion detection systems in terms of accurate and precise classification of dynamically evolving threats. Along with the traditional signature based detection, the supervised and unsupervised machine learning algorithms are also being deployed to detect advance anomalies. However, due to the class overlap between the threat and legitimate data over feature space, satisfactory detection results cannot be obtained. This necessitates the introduction of cognition in the domain of cyber-security. In this paper, a wavelet based multiscale Hebbian learning approach in neural networks is introduced to address the challenge of class overlap. Contrary to inherently linear single scale Hebbian learning, the proposed methodology is able to distinguish non-linear and overlapping classification boundaries sufficiently well. A comparison of presented techniques with fundamental gradient descent based neural network shows promising results. Experimental results on simulated and real-world UNSW-NB15 dataset have been presented to support the claim.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".