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Record W1809627455 · doi:10.1002/sec.727

SRC: a multicore NPU‐based TCP stream reassembly card for deep packet inspection

2013· article· en· W1809627455 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSecurity and Communication Networks · 2013
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceThroughputNetwork packetComputer networkDeep packet inspectionPayload (computing)Stream processingTimeoutReal-time computingEmbedded systemOperating system

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.229
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it