High Performance Software-Hardware Network Intrusion Detection System
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
Network intrusion detection systems (NIDS) and quality of service (QoS) demands have been steadily increasing over the past few years. Current solutions using software become inefficient running on high speed high volume networks and will end up dropping packets. Hardware solutions are available and result in much higher efficiency but present problems such as flexibility and cost. Our proposed system uses a modified version of Snort, a robust widely deployed open-sourced NIDS. It has been found that Snort spends at least 30%-60% of its processing time doing pattern matching. Our proposed system runs Snort in software until it gets to the pattern matching function and then offloads that processing to the field programmable gate array (FPGA). The software can then go on to other processing while it waits for the results from the FPGA. The hardware is able to process data at upto 1.7 GB/s on one Xilinx XC2VP100 FPGA. The design is scaleable and will allow for multiple FPGAs to be used in parallel to increase the processing speed even further.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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