Detection and Prevention of Malicious Requests in ICN Routing and Caching
Bibliographic record
Abstract
Information Centric Networking (ICN) is a new communication paradigm for the upcoming Next Generation Internet (NGI). ICN is an open environment that depends on in-network caching and focuses on contents rather than infrastructures or end-points as in current Internet architectures. These ICN attributes make ICN architectures subject to different types of routing and caching attacks. An attacker sends malicious requests that can cause Distributed Denial of Service (DDoS), cache pollution, and privacy violation of ICN architectures. In this paper, we propose a solution that detects and prevents these malicious requests in ICN routing and caching. This solution allows ICN routers to differentiate between legitimate and attack behaviours in the detection phase based on threshold values. In the prevention phase, ICN routers are able to take actions against these attacks. Our experiments show that the proposed solution effectively mitigates routing and caching attacks in ICN.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".