Pith. sign in

Paper Citation Record · LEDGER

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate

As of 19 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2509.00397.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.00397 v1

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:45:22.027286Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

85 of 85 outbound references displayed

  • verified exact5
  • verified fuzzy68
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 808146ee-e4c6-443c-93d2-32ea135c0f1f · outbound

This paper cites Pensando.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Pensando

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:14.551825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.551825Z digest=sha256:3c4ee470a51bc751b80b25527be2d1efbbd3803a42bf5d7bd38b7efdeb27764b

Observation dadc3b3b-12c8-44ce-ba4d-26773d201d71 · outbound

This paper cites Machine Learning for En- crypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Machine Learning for En- crypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:14.619501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.619501Z digest=sha256:dcfae5fd2f45c16bef3d0f30effeb9863b12972f845ad5cb1ea19df7d2be4ecf

Observation 97d3d645-b62b-4109-ad90-43c28fd5b9ef · outbound

This paper cites Opentuner: An Extensible Framework for Program Autotuning.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Opentuner: An Extensible Framework for Program Autotuning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:14.744583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.744583Z digest=sha256:7accaaf6dc5a0878581179264bf50a02930f3504b07a4335c8404821cb27a9f4

Observation 687fa6a5-a512-4bf4-bba2-0a943dc1a1a1 · outbound

This paper cites Practical Traffic Analysis Attacks on Secure Messaging Applications.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Practical Traffic Analysis Attacks on Secure Messaging Applications

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:14.848848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.848848Z digest=sha256:eec028826a415ee72fd02825fd803dafd0f9ce3fb0fefe18a5acabc8e09dd909

Observation 963e09f2-3ee3-4e90-98ea-1be18ce5a1ec · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:14.924866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.924866Z digest=sha256:ce968245916a0930c3bb12ef5485d775e1f35aff4504461b1f4ab333333d0fb2

Observation 617ece89-a22e-40e9-97c8-e025adcc4fee · outbound

This paper cites Understanding data center traffic characteristics.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Understanding data center traffic characteristics

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:35.328483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.018076Z digest=sha256:00b655910d0822e3617931ba88b0c0199e9e93b4d303659d810a62c5ac02c6c0

Observation 32c08f3b-cbc7-4729-b3c7-97b843652c3f · outbound

This paper cites Bergstra, D.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Bergstra, D

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:35.161459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.123779Z digest=sha256:ea11f7dc4d3434c7794d24d66080ca91261177cde396db565c04596e62061b09

Observation 6e362a41-9021-43af-9341-a40c15626b2b · outbound

This paper cites P4: Programming Protocol-Independent Packet Processors.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate P4: Programming Protocol-Independent Packet Processors

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:34.949513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.199449Z digest=sha256:fc80d5cf2b699f4c54fe9cb5ac5e3b7454df02eaff4e8e2fd07bd76e593827d9

Observation 1316a1b3-5344-49e4-bc49-f32595919601 · outbound

This paper cites Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:34.755367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.353963Z digest=sha256:dd36861566eb59c5307be405ed0ecc392689982d17af85f6f183e875b2bf5cc0

Observation ca783d82-edb3-4da9-87ae-1a82fae2475e · outbound

This paper cites Trident 5 / BCM78800 Series.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Trident 5 / BCM78800 Series

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:34.561128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.451869Z digest=sha256:a4fe9ca106884173ef54379c827761ff2057d1eae79995ab795b2f100949aef2

Observation bf655304-bd9b-4dc8-9a81-4dbee42ae0c1 · outbound

This paper cites Trident4/BCM56880 Series.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Trident4/BCM56880 Series

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:34.360681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.563106Z digest=sha256:db4571d440aeaa62a913ea7b4ba7830e0c76603f5dec81a141c0525adaed434e

Observation 05d2fe06-b58d-4a5f-9a3a-59e4d0177cc2 · outbound

This paper cites pForest: In-Network Inference with Random Forests.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate pForest: In-Network Inference with Random Forests

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:15.647845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:15.647845Z digest=sha256:1b728cfee475940b1dd3dc53c01365c3bf3386c01fceebd237007fc496d0cf6b

Observation 237f2abb-00db-4ac2-a226-5870c1a63625 · outbound

This paper cites CIC IDS 2017 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IDS 2017 Dataset

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:34.095072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.772555Z digest=sha256:b19f1874759b099aa4a4c34315ee0b94369bd20a2c830225e4be3e92e1d335d3

Observation bb1fd775-9cda-42f0-9884-f7dc01a3a21f · outbound

This paper cites CIC IDS 2018 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IDS 2018 Dataset

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:33.841401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.880045Z digest=sha256:49411c9e185bd510d3ec9c3b690deef2130569cbf24b6508ab5f96a1fb94c403

Observation 027f0a3a-35f7-4306-9fda-734e90f55112 · outbound

This paper cites CIC IoMT 2024 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IoMT 2024 Dataset

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:33.593958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:15.976328Z digest=sha256:29b27bcaa09f4af80481927318b6fa78147999aeb864d45ee1b2cd98a305253c

Observation 3952ffdc-076c-4bd3-a427-0b31a72762f7 · outbound

This paper cites CIC IoT 2023 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IoT 2023 Dataset

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:33.363639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.074417Z digest=sha256:49f66d9702bf33ff0ac7b230e06e0d51eecc662a57db2eb856d7f313a5c94127

Observation 8674f8a3-a048-45a6-878f-1038911e2a5a · outbound

This paper cites CIC VPN Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC VPN Dataset

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:33.114248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.173635Z digest=sha256:43d6feea28e1fcb0e19b0d42e54f2e997afb87e44e27c65707a793a2eeb4c292

Observation 31ecd33f-d5fe-4a8f-923f-d6fecfce8a7a · outbound

This paper cites Tensor Processing Units (TPUs).

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tensor Processing Units (TPUs)

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:32.871545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.290927Z digest=sha256:39890362a777d4b50e1dd51df6ad226a1c2de6f6631753ee3ee55f79aac503dc

Observation 627c3739-721e-4fb6-9c89-e77c8d280d8f · outbound

This paper cites Intel P4 Insight.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel P4 Insight

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:32.690700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.369018Z digest=sha256:0d7d03960affadb792736eb2a1af6d0ded56c22659505c2e40e9c797837d2fea

Observation ad111f03-27ff-4a6c-a7ff-a905a5607eb8 · outbound

This paper cites Intel ® P4 Studio.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel ® P4 Studio

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:32.449087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.482291Z digest=sha256:0e9ec37f8a265ff4fb36cccbeb7a0f0fba5339b1ceddf2effccce2f4d8bc417e

Observation f6d3a88f-1f21-4ac0-9cc3-2ecdfd79489d · outbound

This paper cites NVIDIA T4 Tensor Core GPU.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate NVIDIA T4 Tensor Core GPU

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:32.213554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.600791Z digest=sha256:bc15ef72f4118c1fac65e499c432a68500194ba96acf8290bb89dbb809c596ea

Observation 79aea255-d9d3-44ca-a3be-b678d3370a14 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:45:32.108315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.723102Z digest=sha256:23179d4d45839ef52e31167ccd97347001204652fec54b7f4922c7d1859f8416

Observation 681c94fa-f3c4-4d48-a507-756ec66db875 · outbound

This paper cites Brighten Godfrey, and Michael Schapira.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Brighten Godfrey, and Michael Schapira

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:32.017685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.878446Z digest=sha256:08fafcc6aeb0bbafe64b7cef2357e5f30d4d36099f62d28560b2169ee6650c79

Observation 4ef27ebb-3d19-4a8b-a3f3-6951a972910d · outbound

This paper cites HorusEye: A Realtime IoT Malicious Traffic Detec- tion Framework using Programmable Switches.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate HorusEye: A Realtime IoT Malicious Traffic Detec- tion Framework using Programmable Switches

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.828227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:16.955177Z digest=sha256:506c3b71b13418f407327f788b810600883c24571db4d6e0e88c8633bfa57783

Observation d75a6c65-7630-475c-bf2a-102e603afec8 · outbound

This paper cites Doriguzzi-Corin, S.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Doriguzzi-Corin, S

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.679376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.038347Z digest=sha256:d1d269c2c20efe4dfd19b21dd3f362546c753c0e06a5ca2a62dfde66eee60248

Observation f2e3230b-55d4-48c7-86ca-a95def77673d · outbound

This paper cites Moongen: A Scriptable High- Speed Packet Generator.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Moongen: A Scriptable High- Speed Packet Generator

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.499877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.088403Z digest=sha256:9d36053e4b81a3779e2f8e1c99875062308128f23245bc33251e4e3512abb895

Observation 665756db-67d5-4d54-af5f-8a2adb1f8148 · outbound

This paper cites BOHB: Robust and efficient hyperparameter optimization at scale.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate BOHB: Robust and efficient hyperparameter optimization at scale

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.391719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.174082Z digest=sha256:5558360c0a3278dc426e08f42ed93dc250ad5c4498c06e0288f1d89574179254

Observation 61a228ef-9c52-4300-a5f3-a582ab5e725f · outbound

This paper cites Stratum OS.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Stratum OS

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.225380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.264369Z digest=sha256:15197bdcc27d10a3480f1e0db179f01ee55028fc35502b7b9154b940ef02614c

Observation 6752a8a9-1dc3-4797-9685-62a0b20868b2 · outbound

This paper cites Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.118771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.341890Z digest=sha256:b223795a14d72f6f28a2322f8342cfd64b6833856a31f1df71d57daec5da6ea3

Observation 909ad67c-5ff8-46e3-92d0-c3ffd09d2e4a · outbound

This paper cites Network Pro- gramming Language (NPL) Specification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Network Pro- gramming Language (NPL) Specification

Reference 30

Resolution
verified exact
raw_fallback, observed 2026-08-05T13:45:22.990057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.397767Z digest=sha256:e80adaa9ab4560a67c3cd9c8053ef8798a2af9544c84a4a5513057738c52e5e1

Observation f7bc04bb-4be8-4fcd-8453-b10e4379463c · outbound

This paper cites Characterization of encrypted and VPN traffic using time-related features.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Characterization of encrypted and VPN traffic using time-related features

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.984614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.482104Z digest=sha256:52135df36fa3f2d105392be73d35d45f3700253d1de76e22b326f92f60cfb6ec

Observation b0f07b16-dad3-41cd-a02f-92b5f16d125f · outbound

This paper cites CICFlowMeter.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CICFlowMeter

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.839238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.538690Z digest=sha256:cd8d96f202b32587dd54f54bb09c4eea601432faa170bf1b7dc7bcd7473170f6

Observation 10c75ba9-75f0-4a70-a254-b9d840b3f936 · outbound

This paper cites PostgreSQL.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate PostgreSQL

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.727005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.595077Z digest=sha256:505175a8d4678d8700fb8fddc091c9ab2cd7cbd14a76ba469b669e418f52404a

Observation 4eec58aa-cb0a-417e-9b1b-70de1629af72 · outbound

This paper cites Gupta, R.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Gupta, R

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.611022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.681576Z digest=sha256:9cc2b45e847f70bbfc15215b8d9e07bd179252f165bf042496b4038753170231

Observation 27f123ae-f313-4594-99e7-18a3075eacae · outbound

This paper cites netFound: Principled Design for Network Foundation Models.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate netFound: Principled Design for Network Foundation Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:17.761178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:17.761178Z digest=sha256:0e496fd1d2e6ab4790c82a5157809257083e0552c7e8705dee018cb36f84117e

Observation 0dc098d3-1f61-415f-bac4-16d01e3eb220 · outbound

This paper cites CUBIC: A New TCP- Friendly High-Speed TCP Variant.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CUBIC: A New TCP- Friendly High-Speed TCP Variant

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.486041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.833104Z digest=sha256:9ac4e0d1cf30633424f592a561a023dafe70226e714fad81c1f87e772cb51607

Observation d39b64df-3f74-4ca7-bf91-23f221a39b3b · outbound

This paper cites Moore, Gianni Antichi, and Marcin Wój- cik.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Moore, Gianni Antichi, and Marcin Wój- cik

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.367260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.897413Z digest=sha256:6beb0e91959af5ad25cc29199e929ae2b786312495f8350cac7aaf59610b0c6c

Observation 39e26a03-c9a5-4718-ab56-e77258a2a7d5 · outbound

This paper cites Understanding the CRC32 Hash: A Comprehensive Guide.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Understanding the CRC32 Hash: A Comprehensive Guide

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:30.241542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:17.984683Z digest=sha256:7c9a7bd0d6b113652167181e20d1aca8286d600af4541303df619ac7567c1091

Observation 17ec53f9-01ad-465d-9c1d-04fcb657f665 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:45:30.098838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.036928Z digest=sha256:ccf12a52cd067a02670017d6518a6e3ff27e0e8e2693a0ef4b1577dea710842c

Observation c27e5d5a-2c47-4c01-8fdf-4587c36f91a8 · outbound

This paper cites Intel Ethernet Network Adapter X710.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel Ethernet Network Adapter X710

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.983704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.108327Z digest=sha256:20390426054d31d8abbed81dfc61e25a6aa1c72f94ac16849b071ea6edc409f8

Observation bcb2d14d-9136-4523-92eb-954c6c9200a3 · outbound

This paper cites Tofino: P4-programmable Ethernet switch ASIC that delivers better performance at lower power.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tofino: P4-programmable Ethernet switch ASIC that delivers better performance at lower power

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.852578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.167784Z digest=sha256:e4602f4c5ac19400bb2b49cf95a9da05cb28daf2607937194d7f14727a7e1680

Observation 1de7dec6-d868-4873-8c86-b72d8d9fe2a7 · outbound

This paper cites Tofino2: Second-generation P4-programmable Ethernet Switch ASIC that Continues to De- liver Programmability without Compromise.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tofino2: Second-generation P4-programmable Ethernet Switch ASIC that Continues to De- liver Programmability without Compromise

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.708640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.232948Z digest=sha256:0f1269cd20ab9033dfd5a8bb57ceae36a38f4e208aeb1dd9274f6f2b4e41f027

Observation 5416891b-ae8c-4be9-a527-842057a29c46 · outbound

This paper cites Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.560049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.306268Z digest=sha256:fe7de20e262befbbb050e290053c9e39f6f08e0c19e8e8d069d68576e1eb7a3a

Observation bea22191-251b-48c0-82ba-cc43a86cf898 · outbound

This paper cites AC-DC: Adaptive Ensemble Classification for Network Traffic Identification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate AC-DC: Adaptive Ensemble Classification for Network Traffic Identification

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:45:22.782341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.370037Z digest=sha256:4bf48e02075ed512f7053b0547e3624b0d48e69b468d3de6a423672003f89ad0

Observation 1369be95-4706-4508-b38f-cff54a32e534 · outbound

This paper cites GPflowOpt: A Bayesian Optimization Library using TensorFlow.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate GPflowOpt: A Bayesian Optimization Library using TensorFlow

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:45:22.613928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.445106Z digest=sha256:661f3f0cffca0fa702f4164445af2286fae7eecb1e8d264fb1db425bec06e57e

Observation f0d7eb92-8b22-4a0d-9a69-94e4f235a27b · outbound

This paper cites The IPU: A New, Strate- gic Resource for Cloud Service Providers.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate The IPU: A New, Strate- gic Resource for Cloud Service Providers

Reference 46

Resolution
verified exact
raw_fallback, observed 2026-08-05T13:45:22.465472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.520638Z digest=sha256:7351d582532b3c481294b19753a20ace5102200deb4123b9426e0a1b2c2c9db4

Observation 4de58cdc-3f2f-4c45-a6e4-99df39e0ee15 · outbound

This paper cites Characterization of tor traffic using time based features.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Characterization of tor traffic using time based features

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.414068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.615821Z digest=sha256:4ee4ebc9f74223f695e6776c43bc2c0e93daa691355c25d1898856d10a7c15d2

Observation 96720761-d142-4e96-b7b6-dc9eaf710d97 · outbound

This paper cites HPCC: High Precision Congestion Control.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate HPCC: High Precision Congestion Control

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.298770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.715144Z digest=sha256:7d96e2eb78c4459ebc2223b9730388b338275979f7b9461d8ad79590571124e6

Observation 1950a001-48ff-465a-94e7-d9fcf3fea4b5 · outbound

This paper cites SMAC3: A versatile Bayesian optimization package for hyperparameter optimiza- tion.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate SMAC3: A versatile Bayesian optimization package for hyperparameter optimiza- tion

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.190622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.823519Z digest=sha256:1533962ec92a09c9bf5a511230cd21088e68176ad08c41d2ee7f635713c693be

Observation 664147f6-555a-4613-a885-553dac66eca5 · outbound

This paper cites ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:45:22.179777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:18.944491Z digest=sha256:94370a9c25c31e6bd1d4e16a039c80a1de91b351a11f27f6c706a95777e9b17e

Observation c4d95599-d8f6-4373-8465-7270f7117b6e · outbound

This paper cites Neural Adaptive Video Streaming with Pensieve.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Neural Adaptive Video Streaming with Pensieve

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:29.073444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.047569Z digest=sha256:8c360e594d12a7c04857fec53e68f55fb1b0f3c91a1b12f1687913223c1bb8a6

Observation 05248c1a-a765-48b7-9dca-1a615088ca80 · outbound

This paper cites Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.934399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.151703Z digest=sha256:4e8120be45d9e310dff125d5eb9cf86404715daab1cbbbdde912638a0ea582f0

Observation 7a5ba5b7-903b-4eaf-a2cb-ce5c39fae37a · outbound

This paper cites Algorithmic Performance- accuracy Trade-off in 3D Vision Applications using Hyper- mapper.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Algorithmic Performance- accuracy Trade-off in 3D Vision Applications using Hyper- mapper

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.823446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.243830Z digest=sha256:55c5bac753c3df0979cbe4e18067632233f3b2b6dcbd6d5a1eca047c7012b0f8

Observation 98907fd1-56a3-48ba-a7e3-6463c2cebfe1 · outbound

This paper cites ConnectX-6 Network Adapters.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate ConnectX-6 Network Adapters

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.719366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.313833Z digest=sha256:6ef62b0b36c407d99a9c29bf7d6c5858ca140858c0bf26556f32721f80468bfd

Observation a9c1de6a-b9a1-4209-a2b0-74f5c7c96177 · outbound

This paper cites DOCA Documentation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate DOCA Documentation

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.612437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.398282Z digest=sha256:0be37d312323609d5ebf0300fe0bd3a616cea0a3b7b0d36a3f52b61647943975

Observation 40fc3a25-8286-4dc2-9451-ca28e57bdc90 · outbound

This paper cites Nvidia BlueField Data Processing Units.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Nvidia BlueField Data Processing Units

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.374338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.463800Z digest=sha256:2951e97142396b494acb8117930dd210dafffc0904dbe70d472c6d02c29d9b05

Observation 4f7e175a-6c3c-4af9-8959-985f4f7352fd · outbound

This paper cites NVIDIA Spectrum-X: Ethernet Networking Platform for AI.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate NVIDIA Spectrum-X: Ethernet Networking Platform for AI

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:28.033527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.554337Z digest=sha256:ec3d2292b2d3fa4eab00f346a9f2e5e7e713e7e1abac2adce904f0f6f9809725

Observation 514f7972-2fd1-4799-b9a7-d8d46c908cf0 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:45:27.893249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.626642Z digest=sha256:a3687fad9e97ba743ba2b6284efed7026b27203a3465fb7135abd12976fa868a

Observation 9be831dc-268a-4808-9135-f8b5463468c0 · outbound

This paper cites Scikit-learn: Machine learning in Python.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Scikit-learn: Machine learning in Python

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:27.765428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.670380Z digest=sha256:2cb33f6ea7319ff98615fc4c8be43b8198b144836e2acc17a1d21a6b8e914ea5

Observation d0a50a44-2eaa-44bf-a1cb-0703bf507742 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 60

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:45:27.640731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.744699Z digest=sha256:8dafacaee73513fb5252d25c92ed6d2cf081b363ec002f0ebbfd417668179db6

Observation d4e03497-10c5-426b-829a-d2d0d6973517 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:45:27.485303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.830754Z digest=sha256:30243622d2e36dce3d5198ce96e33718bfab81a6ed307fb3fbc580721e5720b8

Observation 84e876dd-1ef8-4b05-9252-d3782d8dfc31 · outbound

This paper cites Elastic RSS: Co-Scheduling Packets and Cores Using Programmable NICs.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Elastic RSS: Co-Scheduling Packets and Cores Using Programmable NICs

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:27.357372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:19.907983Z digest=sha256:51859870dbb320125b9fc6a624451eb040325352e94ed73019bd227de20fecdb

Observation 82e2a517-9082-4590-b13b-dec04df60ee8 · outbound

This paper cites The case for an intermediate representation for programmable data planes.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate The case for an intermediate representation for programmable data planes

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:27.203207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.047613Z digest=sha256:62891c8d46fc9bf98a1f60c3b69acdb45a9ca5f145af2664ab459f8797781da4

Observation d23be946-2366-4ad7-b7b6-68ad4c3e9666 · outbound

This paper cites Query planning for robust and scalable hybrid network telemetry systems.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Query planning for robust and scalable hybrid network telemetry systems

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:27.061473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.166244Z digest=sha256:08109b36b3594343bdedc0f6bcdeeeb1bac97699bffde986ad4cce608936d52b

Observation abf60c31-84e1-47f0-99ad-48c12873d829 · outbound

This paper cites Exploring Hyperparameter Usage and Tuning in Machine Learning Re- search.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Exploring Hyperparameter Usage and Tuning in Machine Learning Re- search

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:26.920523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.293409Z digest=sha256:60566edfdb0c3b81333f2bc371f7a404b8f0d0afd589b1790f8d2ac85abf392c

Observation c09589b8-7830-4fcf-8bd1-8ba7c4f4075d · outbound

This paper cites Re-architecting Traffic Analysis with Neural Network Interface Cards.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Re-architecting Traffic Analysis with Neural Network Interface Cards

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:26.749518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.382155Z digest=sha256:13b3b26a825ace937fc5ada28dfd5287ea54b236001c6445a644dedc2d0a93a2

Observation b942314a-cae7-4e09-96bf-890e8dfff44d · outbound

This paper cites Taurus: A Data Plane Architecture for Per-Packet ML.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Taurus: A Data Plane Architecture for Per-Packet ML

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:26.636939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.451488Z digest=sha256:d5f9787cdc21b0482661c412c74a0dc78c5eddc7c979933c2c36f8b20e7c15ac

Observation da7010ac-5166-44a0-aaf8-2f15c0077484 · outbound

This paper cites Homunculus: Auto-Generating Ef- ficient Data-Plane ML Pipelines for Datacenter Networks.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Homunculus: Auto-Generating Ef- ficient Data-Plane ML Pipelines for Datacenter Networks

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:26.445323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.554337Z digest=sha256:12a75abf45d5fb470d0e206527454ce197464daa1a80de1635ae957a0b07cd20

Observation 2d928422-0bfd-49d2-8592-eeb83889e34b · outbound

This paper cites Tensorflow.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tensorflow

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:26.149665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.655981Z digest=sha256:82458f247481e44d6d60d562ed24477a31af336c48eee74dcb6a3dcf8b5bb967

Observation d8c862b8-c7cd-498c-b190-9f82f0681430 · outbound

This paper cites Malware traffic classification using convo- lutional neural network for representation learning.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Malware traffic classification using convo- lutional neural network for representation learning

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.862689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.733084Z digest=sha256:2d37163f9641b1b49005f269444e77daa1909d207afc39be6048bf0028efba9c

Observation 6f698f23-b943-4d39-9a10-c467554e7e38 · outbound

This paper cites xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.765123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.823352Z digest=sha256:1754c59b2047d7a8e5935a7603601b277127e6c62200a86058db47c779bd641f

Observation d30a3bb7-c8b2-458e-9d65-bf542ec79cdd · outbound

This paper cites Bayesian Optimization.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Bayesian Optimization

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.662325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:20.928027Z digest=sha256:9a9c048f7cd93799f77e51f004ceb443960761281f792c77e70393f754532359

Observation f99a18e4-c995-4ec1-83df-63ffb336f878 · outbound

This paper cites TCP ex machina: Computer-generated Congestion Control.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate TCP ex machina: Computer-generated Congestion Control

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.469326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.021679Z digest=sha256:efa1f1d5f1a0216b5357c48f31f44092c4b71305c6e91424f82aed57e4ac1ac3

Observation d07ce629-9a4b-455a-8d82-0e27e9d7e768 · outbound

This paper cites A GPU-accelerated network traf- fic monitoring and analysis system.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate A GPU-accelerated network traf- fic monitoring and analysis system

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.292798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.123041Z digest=sha256:a879a30785aa031b2c2b7653edf067f0d465a63545e8c7270ad385706cd451dc

Observation 55254f05-b1a4-4fbf-a4e4-030a5e043c9c · outbound

This paper cites Mousika: Enable General In-Network Intel- ligence in Programmable Switches by Knowledge Distillation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Mousika: Enable General In-Network Intel- ligence in Programmable Switches by Knowledge Distillation

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.049125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.232594Z digest=sha256:3d747ec3099080b5a3712ec7bf8d2bef97f7c5de93dc7343d15220b41db601f9

Observation 789c6f2a-7269-483b-9e89-681ee183363e · outbound

This paper cites Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Net- work Environments with TCP-Aware Traffic Augmentation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Net- work Environments with TCP-Aware Traffic Augmentation

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.744171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.320640Z digest=sha256:92b2651bfc49eef4531624ea08750a19316ce1e34bd5c515da9e6df7f600c9a8

Observation 431cbf71-b0af-4edb-8c67-11accd4349bc · outbound

This paper cites Alveo SN1000 SmartNICs.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Alveo SN1000 SmartNICs

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.413963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.414824Z digest=sha256:d02ef6acac5636a137caf274a4223873cd12f0939fd5ba31a8236132b44faf0f

Observation 968cf07c-4fea-4950-baa3-d4bec2164def · outbound

This paper cites Alveo U250 Data Center Accelerator Card.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Alveo U250 Data Center Accelerator Card

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.221561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.502939Z digest=sha256:e99fac40deb055fdb6211a25e993eb6ef152e9703f7663fab5a879dfc1cb8870

Observation 04093fc1-d0b6-4150-9abc-9d690cf2a8af · outbound

This paper cites Do Switches Dream of Machine Learning? Toward In-Network Classification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Do Switches Dream of Machine Learning? Toward In-Network Classification

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.042193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.570990Z digest=sha256:6e09e4b59963bfa52e30233d7f06a60015e6806b6cb135fab78e8909c4f5ccad

Observation bbf77c45-b561-4618-93c8-14b7e6a12eb3 · outbound

This paper cites X2 Programmable Ethernet Switch.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate X2 Programmable Ethernet Switch

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.923411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.648145Z digest=sha256:6914a3b14b68068d1077d3054ad85c0febe71eaf87ecb73d48c34b7d956e3673

Observation 133521e3-3ee9-4ec1-b80e-8cdcac38cd80 · outbound

This paper cites Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.742085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.725718Z digest=sha256:02a80d83342e50c93c525af37785d3882db87165b2990594af242e0a7ae4b8a2

Observation ef6795ee-84c0-43aa-9674-d033837aded4 · outbound

This paper cites Pantheon: The Training Ground for Internet Congestion-Control Research.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Pantheon: The Training Ground for Internet Congestion-Control Research

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.581985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.799138Z digest=sha256:3e42119f2cca0789ce6656eab3fe573e1c65f0af0e4fe76c2fbe5035cc2a83ad

Observation f76b5e96-186a-48e9-a369-040175af36e5 · outbound

This paper cites Brain-on-switch: towards advanced intelligent network data plane via NN-driven traffic analysis at line-speed.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Brain-on-switch: towards advanced intelligent network data plane via NN-driven traffic analysis at line-speed

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.418855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.870854Z digest=sha256:3a694ad0ac71664e4689581d33d9bcdafbd2d7917fc97cad9a6796acddf34b3f

Observation 156998ce-7612-4db1-a24b-6955e5f1ac8f · outbound

This paper cites Planter: Rapid prototyping of in-network machine learning inference.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Planter: Rapid prototyping of in-network machine learning inference

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.282629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:21.956057Z digest=sha256:6636b9868b89a21e37a98f708e0b9c9725078ee419642182f1ac5af734ba50ce

Observation da5e728f-7137-4b8a-a110-d60b50863a3f · outbound

This paper cites An Efficient Design of Intelligent Network Data Plane.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate An Efficient Design of Intelligent Network Data Plane

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.119357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T13:45:22.027286Z digest=sha256:205bb22408eea3957bcf7f11f2f9a6481d80217a332905f962de6fe61653c5fd

Pith citing papers

No inbound Pith citation observations are available.