A chaotic measure for cognitive machine classification of distributed denial of service attacks
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
Today's evolving cyber security threats demand new, modern, and cognitive computing approaches to network security systems. In the early years of the Internet, a simple packet inspection firewall was adequate to stop the then-contemporary attacks, such as Denial of Service (DoS), ports scans, and phishing. Since then, DoS has evolved to include Distributed Denial of Service (DDoS) attacks, especially against the Domain Name Service (DNS). DNS based DDoS amplification attacks cannot be stopped easily by traditional signature based detection mechanisms because the attack packets contain authentic data, and signature based detection systems look for specific attack-byte patterns. This paper proposes a chaos based complexity measure and a cognitive machine classification algorithm to detect DNS DDoS amplification attacks. In particular, this paper computes the Lyapunov exponent to measure the complexity of a flow of packets, and classifies the traffic as either normal or anomalous, based on the magnitude of the computed exponent. Preliminary results show the proposed chaotic measure achieved a detection (classification) accuracy of about 66%, which is greater than that reported in the literature. This approach is capable of not only detecting offline threats, but has the potential of being applied over live traffic flows using DNS filters.
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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 | 0.000 | 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