A Composite Malicious Peer Eviction Mechanism for Super-P2P Systems
Bibliographic record
Abstract
Large-scale P2P applications (e.g., social networking, online gaming, video streaming) that host millions of users increasingly rely upon semi-structured super-P2P systems to provide efficient services in dynamic environments. Given the critical role of 'super peers' in such topologies, attackers target super peers due to the resultant high damage on P2P services. In this paper, we consider the prominent class of Outgoing Eclipse Attacks (OEA) where an attacker aims to block the communication by controlling all the outgoing connections of honest super peers. Our interest on OEA stems from the fact that our simulation studies reveal that OEAs can cause up to 90% of all service requests to fail. Our attack mitigation relies upon a novel (a) monitoring and (b) malicious peer eviction scheme based on a composite proactive and reactive mechanism. Our proactive mechanism enforces an upper bound on the number of connections an attacker can establish, whereas our reactive mechanism expels malicious peers from the overlay using a distributed consensus protocol. We show that our protection mechanism is highly effective and exhibits a low false-positive rate. Our extensive simulation study validates the analytical results over a large range of parameters with observed detection accuracies of 99% and throughput enhancements of up to 100% while entailing an overhead of less than 5%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".