Paper Citation Record · LEDGER

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks

As of 18 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2605.02987.

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

pith.paper-citation-record.v1
2605.02987 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:53:08.474838Z

measured 19 of 19 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

19 of 19 outbound references displayed

verified exact0
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 46ccc8c7-1c15-460d-a6e8-9e1030294c9d · outbound

This paper cites IPCA-SAMKNN: A Novel Network IDS for Resource Constrained Devices.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks IPCA-SAMKNN: A Novel Network IDS for Resource Constrained Devices

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.191541Z

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.

Observation 332cc27c-26b9-44fa-9587-2712208dfcbf · outbound

This paper cites Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.155196Z

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.

Observation d6aa3031-82b3-463d-afad-133ad16ef3d5 · outbound

This paper cites A Lightweight Supervised Intrusion Detection Mechanism for IoT Networks.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks A Lightweight Supervised Intrusion Detection Mechanism for IoT Networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.159523Z

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.

Observation b1c2096f-8841-482d-810e-f74e7246db3e · outbound

This paper cites A Lightweight Hybrid Approach for Intrusion Detection Systems Using a Chi-Square Feature Selection Approach in IoT.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks A Lightweight Hybrid Approach for Intrusion Detection Systems Using a Chi-Square Feature Selection Approach in IoT

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.170439Z

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.

Observation 228d7078-22ad-4375-9eb5-2202f21e8523 · outbound

This paper cites Intrusion Detection for IoT Network Security with Deep Neural Network.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Intrusion Detection for IoT Network Security with Deep Neural Network

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.163135Z

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.

Observation 292e421c-aaf7-40c3-9bae-0b4e39e850b4 · outbound

This paper cites IoT Intrusion Detection Using Machine Learning with a Novel High Per- forming Feature Selection Method.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks IoT Intrusion Detection Using Machine Learning with a Novel High Per- forming Feature Selection Method

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.183211Z

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.

Observation 07b8b3e7-81d9-47b9-beee-aa9665a26381 · outbound

This paper cites Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.149468Z

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.

Observation f0f20775-642e-4459-82ad-5bb355de23c8 · outbound

This paper cites Implementation of Intrusion Detection Model for DDoS Attacks in Lightweight IoT Networks.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Implementation of Intrusion Detection Model for DDoS Attacks in Lightweight IoT Networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.179376Z

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.

Observation 438cb236-43b1-45fe-b013-ee4de0fa7fe7 · outbound

This paper cites A Systematic Literature Review of Recent Lightweight Detection Approaches Leveraging Machine and Deep Learning Mechanisms in Internet of Things Networks.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks A Systematic Literature Review of Recent Lightweight Detection Approaches Leveraging Machine and Deep Learning Mechanisms in Internet of Things Networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.175382Z

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.

Observation 958cd1e4-34c9-4265-ab09-2d5bd1dcaa12 · outbound

This paper cites DFE: Efficient IoT Network Intrusion Detection Using Deep Feature Extraction.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks DFE: Efficient IoT Network Intrusion Detection Using Deep Feature Extraction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.187425Z

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.

Observation 9c9d24bd-8469-4398-811e-1b42b730b157 · outbound

This paper cites UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.166784Z

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.

Observation a68d370a-8a2f-4fa9-acc3-ac1d920924e9 · outbound

This paper cites Attack Classification Using Machine Learning on UNSW-NB15 Dataset Using XGBoost Feature Selection and Ablation Analysis.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Attack Classification Using Machine Learning on UNSW-NB15 Dataset Using XGBoost Feature Selection and Ablation Analysis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.205588Z

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.

Observation 702d98f7-d3a5-4f4c-9ca1-8189ecb61f99 · outbound

This paper cites The Effect of Recursive Feature Elimination with Cross-Validation (RFECV) Feature Selection Algorithm toward Classifier Performance on Credit Card Fraud Detection.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks The Effect of Recursive Feature Elimination with Cross-Validation (RFECV) Feature Selection Algorithm toward Classifier Performance on Credit Card Fraud Detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.199221Z

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.

Observation 428c9697-6874-4002-9dec-69cb2f49b15b · outbound

This paper cites XGBoost Feature Selection for Multi-Class and Binary Classification on UNSW-NB15 Dataset.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks XGBoost Feature Selection for Multi-Class and Binary Classification on UNSW-NB15 Dataset

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.209520Z

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.

Observation 01da8a50-a581-4ad1-9c6f-dfdae10c64f9 · outbound

This paper cites Ensemble Learning for Intrusion Detection Systems: A Systematic Mapping Study and Cross-Benchmark Evalua- tion.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Ensemble Learning for Intrusion Detection Systems: A Systematic Mapping Study and Cross-Benchmark Evalua- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.216255Z

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.

Observation 6b4e575b-6fe8-46ea-a692-7e3730da8896 · outbound

This paper cites Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.224828Z

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.

Observation 4340d403-5432-4b46-be24-b2c4503bb1a8 · outbound

This paper cites A Lightweight IoT Intrusion Detection Method Based on Two-Stage Feature Selection and Bayesian Optimization.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks A Lightweight IoT Intrusion Detection Method Based on Two-Stage Feature Selection and Bayesian Optimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.220758Z

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.

Observation 19ffa76b-4c21-472e-a991-db1b670addbf · outbound

This paper cites Lightweight Intrusion Detection System for IoT with Improved Feature Engineering and Advanced Dynamic Quantization.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Lightweight Intrusion Detection System for IoT with Improved Feature Engineering and Advanced Dynamic Quantization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.195625Z

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.

Observation 24566195-fc0c-4b1d-b39a-9898104257d5 · outbound

This paper cites Optimized IoT Intrusion Detection using Machine Learning Technique.

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks Optimized IoT Intrusion Detection using Machine Learning Technique

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:56:40.228800Z

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.

Pith citing papers

No inbound Pith citation observations are available.