A bio-inspired and AI-driven approach to DDoS detection
Bibliographic record
Abstract
Abstract For implementing Internet Protocol version 6 (IPv6), a key protocol referred to as Internet Control Message Protocol version 6 (ICMPv6) is utilized for inherent IPv6 services. Hence, proper detection and mitigation techniques need to be implemented to monitor the security issues associated with ICMPv6 messages. One of the most targeted forms of attacks is the ICMPv6-based Distributed Denial of Service (DDoS) attacks. In order to attain our objective, we have proposed BioDQN, an advanced artificial intelligence-based approach that incorporates adaptive feature selection for ICMPv6 DDoS Detection using Reinforcement Learning (RL) and Genetic Algorithm (GA). Our approach comprises a bio-inspired feature selection module that incorporates an RL and GA mechanism for optimally selecting feature subsets from the input dataset. The experimental findings suggested that among the various classifiers, the transformer model exhibited the peak detection accuracy of 94.2% with an F1 and Area Under the Curve (AUC) score of 0.95 and 0.91, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".