A Performance Evaluation of Hash Functions for IP Reputation Lookup Using Bloom Filters
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
IP reputation lookup is one of the traditional methods for recognition of blacklisted IPs, i.e., IP addresses known to be sources of spam and malware-related threats. Its use however has been rapidly increasing beyond its traditional domain reaching various IP filtering tasks. One of the solutions able to provide a necessary scalability is a Bloom filter. Efficient in memory consumption, Bloom filters provide a fast membership check, allowing to confirm a presence of set elements in a data structure with a constant false positive probability. With the increased usage of IP reputation check and an increasing adoption of IPv6 protocol, Bloom filters quickly gained popularity. In spite of their wide application, the question of what hash functions to use in practice remains open. In this work, we investigate a 10 cryptographic and non-cryptographic functions for on their suitability for Bloom filter analysis for IP reputation lookup. Experiments are performed with controlled, randomly generated IP addresses as well as a real dataset containing blacklisted IP addresses. Based on our results we recommend two hash functions for their performance and acceptably low false positive rate.
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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 | 0.001 | 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 it