P2P-AIS: A P2P Artificial Immune Systems architecture for detecting DDoS flooding attacks
Bibliographic record
Abstract
The human immune system (HIS) plays an important role in protecting the human body from various intruders ranging from naive germs to the most sophisticated viruses. It acts as an intrusion detection and prevention system (IDPS) for the human body and detects anomalies that make the body deviate from its normal behavior. This inspired researchers to build artificial immune systems (AISes) which imitate the behavior of the HIS and are capable of protecting hosts or networks from attacks. An artificial immune system (AIS) is capable of detecting novel attacks because it is trained to differentiate between the normal behavior (self) and the abnormal behavior (non-self) during a tolerization (i.e training) period. Although several AISes have been proposed, only a few make use of collaborative approaches. In this paper we propose P2P-AIS, a P2P approach for AISes in which peers exchange intrusion detection experience in order to enhance attack detection and mitigation. P2P-AIS implements Chord as a distributed hash table (DHT) protocol to organize the peers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".