SRC: a multicore NPU‐based TCP stream reassembly card for deep packet inspection
Bibliographic record
Abstract
ABSTRACT Stream reassembly is the premise of deep packet inspection, regarded as the core function of network intrusion detection system and network forensic system. As moving packet payload from one block of memory to another is essential for the reason of packet disorder, throughput performance is very vital in stream reassembly design. In this paper, a stream reassembly card (SRC) is designed to improve the stream reassembly throughput performance. The designed SRC adjusts the sequence of packets on the basis of the multicore network processing unit by managing and reassembling streams through an additional level of buffer. Specifically, three optimistic techniques, namely stream table dispatching, no‐locking timeout, and multichannel virtual queue, are introduced to further improve the throughput. To address the critical role of memory size in SRC, the relationship between the system throughput and memory size is analyzed. Extensive experiments demonstrate that the proposed SRC achieves more than 3 Gbps in terms of reassembly and submission throughput and triply outperforms the traditional server‐based architecture with a lower cost. Copyright © 2013 John Wiley & Sons, Ltd.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".