A bio-inspired and AI-driven approach to DDoS detection
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 it