Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series
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
Growing global dependence over cyberspace has given rise to intelligent malicious threats due to increasing network complexities, inherent vulnerabilities embedded within the software and the limitations of existing cyber security systems to name a few. Malicious cyber actors exploit these vulnerabilities to carry out financial fraud, steal intellectual property and disrupt the delivery of essential online services. Unlike physical security, cyberspace is very difficult to secure due to the replacement of traditional computing platforms with sophisticated cloud computing and virtualization. These complex systems exhibit an increasing degree of complexity in tracking an attack or monitoring possible threats which is becoming intractable with the existing security firewalls and intrusion detection systems. In this paper, authors present a novel complexity detection technique using generalized multifractal singularity spectrum which is able to not only capture the growing complexity of the internet time series but also distinguishes the presence of an attack accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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