Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders | Paper Citation Record

Observation ed5fe2c9-6aed-4ecc-a029-a59d7641d78a · outbound

This paper cites Zero-day attack detection: a systematic literature review.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Zero-day attack detection: a systematic literature review

Reference 1

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Observation 1149cfc3-dc4f-4006-bedc-503d567a3e5b · outbound

This paper cites Deep learning technique-enabled web application firewall for the detection of web attacks.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Deep learning technique-enabled web application firewall for the detection of web attacks

Reference 2

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Observation eb8d4ca1-27e9-4d8a-879d-6a135d2dbdf3 · outbound

This paper cites A systematic literature review of information security in chatbots.Applied Sciences 2023, 13, 6355.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A systematic literature review of information security in chatbots.Applied Sciences 2023, 13, 6355

Reference 3

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Observation 810c09e4-f0ed-44dc-b61a-2709fdd77b2e · outbound

This paper cites Machine learning for web vulnerability detection: the case of cross-site request forgery.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Machine learning for web vulnerability detection: the case of cross-site request forgery

Reference 4

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Observation 2c8fd990-6f7f-4f87-ac26-ba6e275d8b31 · outbound

This paper cites Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.224379Z

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Observation ee986488-ea67-4884-8621-df3a67737d20 · outbound

This paper cites A survey of network anomaly detection techniques.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A survey of network anomaly detection techniques

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.211017Z

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Observation cb37af0e-7dc9-4021-9ac6-b6106ac5fbc0 · outbound

This paper cites A hybrid unsupervised clustering-based anomaly detection method.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A hybrid unsupervised clustering-based anomaly detection method

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.196726Z

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Observation ae46d9e4-4dd4-4db7-a09e-c29c994bc520 · outbound

This paper cites An efficient algorithm and tool for detecting dangerous website vulnerabilities.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders An efficient algorithm and tool for detecting dangerous website vulnerabilities

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.183099Z

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Observation e5f4cf8b-0a4b-4579-8cd0-e135f36cb925 · outbound

This paper cites Learning DFA representations of HTTP for protecting web applications.Computer Networks 2007, 51, 1239–1255.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Learning DFA representations of HTTP for protecting web applications.Computer Networks 2007, 51, 1239–1255

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.168983Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a215e952-86a4-4d70-b031-03255911ee63 · outbound

This paper cites Web intrusion detection using character level machine learning approaches with upsampled data.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Web intrusion detection using character level machine learning approaches with upsampled data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.155634Z

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Observation d8a88767-6724-4929-9cc9-fa4357efff10 · outbound

This paper cites PF-TL: Payload feature-based transfer learning for dealing with the lack of training data.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders PF-TL: Payload feature-based transfer learning for dealing with the lack of training data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.140852Z

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Observation 7b5d732d-629e-40fd-a5cc-94ce4e82f79c · outbound

This paper cites An anomaly detection method to detect web attacks using stacked auto-encoder.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders An anomaly detection method to detect web attacks using stacked auto-encoder

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.126023Z

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Observation ae4af4fc-5b56-4ef9-8a0d-0a84edb1714c · outbound

This paper cites HMMPayl: An intrusion detection system based on Hidden Markov Models.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders HMMPayl: An intrusion detection system based on Hidden Markov Models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.112043Z

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Observation e9207058-880a-43a6-be9e-1b9ed588ee9e · outbound

This paper cites Anomaly-based web attack detection: a deep learning approach.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Anomaly-based web attack detection: a deep learning approach

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.098623Z

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Observation 76886078-a362-470a-9be7-3de7ebb114d1 · outbound

This paper cites DeepWAF: detecting web attacks based on CNN and LSTM models.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders DeepWAF: detecting web attacks based on CNN and LSTM models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.084210Z

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Observation 8a5a221b-1f0f-4841-ba04-3e6ab55ae4ca · outbound

This paper cites Zerowall: Detecting zero-day web attacks through encoder-decoder recurrent neural networks.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Zerowall: Detecting zero-day web attacks through encoder-decoder recurrent neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.070286Z

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Observation 8f7f9181-9601-459e-be94-8ad845d32f76 · outbound

This paper cites Robust ensemble machine learning model for filtering phishing URLs: Expandable random gradient stacked voting classifier (ERG-SVC).

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Robust ensemble machine learning model for filtering phishing URLs: Expandable random gradient stacked voting classifier (ERG-SVC)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.056507Z

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Observation 13c608bf-f3a0-424d-b897-44f6530e9e6e · outbound

This paper cites Model uncertainty based annotation error fixing for web attack detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Model uncertainty based annotation error fixing for web attack detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.043165Z

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Observation 82f38b18-63f0-4acc-a520-2e26ae132317 · outbound

This paper cites A novel architecture for web-based attack detection using convolutional neural network.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A novel architecture for web-based attack detection using convolutional neural network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.028864Z

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Observation 459b84a3-039c-4495-ae78-91020455d599 · outbound

This paper cites SWAF: a smart web application firewall based on convolutional neural network.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders SWAF: a smart web application firewall based on convolutional neural network

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:41.013700Z

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Observation 4b519a20-b4fb-488a-af22-c5bc37b68bef · outbound

This paper cites Web attacks detection using stacked generalization ensemble for LSTMs and word embedding.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Web attacks detection using stacked generalization ensemble for LSTMs and word embedding

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.999817Z

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Observation 9414dec6-4161-4004-b44f-8b476987efb3 · outbound

This paper cites MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.985420Z

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Observation 2938f770-2ad2-4f34-a730-c1609a35f62c · outbound

This paper cites A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.971225Z

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Observation f8a7a0fc-6d45-46e5-987c-c42c4409211c · outbound

This paper cites Synthesis of Allowlists for Runtime Protection against SQLi.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Synthesis of Allowlists for Runtime Protection against SQLi

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.957309Z

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Observation c68b3c4e-08f2-4546-8f14-669e20b9af26 · outbound

This paper cites Splendor: Static Detection of Stored XSS in Modern Web Applications.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Splendor: Static Detection of Stored XSS in Modern Web Applications

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.943477Z

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Observation 4813dcaa-2aa1-4465-bbfd-8e7b14c8dce6 · outbound

This paper cites Towards a SQL Injection Vulnerability Detector Based on Session Types.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Towards a SQL Injection Vulnerability Detector Based on Session Types

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.929786Z

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Observation 3d6a4c2e-6115-48df-b5f4-87e019e9ee4f · outbound

This paper cites Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM Model.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM Model

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.915505Z

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Observation bc56b58e-d762-47ec-a5c3-c1dfca8574c2 · outbound

This paper cites One-class IoT anomaly detection system using an improved interpolated deep SVDD autoencoder with adversarial regularizer.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders One-class IoT anomaly detection system using an improved interpolated deep SVDD autoencoder with adversarial regularizer

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.901347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a1ecb1b4-1d97-480a-9525-ec3f6b11cff6 · outbound

This paper cites Stacking an autoencoder for feature selection of zero-day threats.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Stacking an autoencoder for feature selection of zero-day threats

Reference 29

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2dd98ab-3581-4766-8d5f-a37bb2cba92d · outbound

This paper cites Deep learning architecture for detecting SQL injection attacks based on RNN autoencoder model.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Deep learning architecture for detecting SQL injection attacks based on RNN autoencoder model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.887492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d801f392-afb4-4a20-ac76-b0acdc373ba7 · outbound

This paper cites Ae-net: Novel autoencoder-based deep features for sql injection attack detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Ae-net: Novel autoencoder-based deep features for sql injection attack detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.872605Z

Source-reported events for the cited work

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Observation 7c1e7fca-66db-4f3c-9826-cb4072db03fc · outbound

This paper cites A lightweight intelligent network intrusion detection system using one-class autoencoder and ensemble learning for IoT.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A lightweight intelligent network intrusion detection system using one-class autoencoder and ensemble learning for IoT

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.858506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4404cb50-fbf9-4a22-8bfa-3be9f5ea22ef · outbound

This paper cites Multi-Class Intrusion Detection System using Deep Learning.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Multi-Class Intrusion Detection System using Deep Learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.844042Z

Source-reported events for the cited work

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Observation 20b0bb7e-9adc-4227-9ea7-acbfd67ef474 · outbound

This paper cites An enhanced deep learning based framework for web attacks detection, mitigation and attacker profiling.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders An enhanced deep learning based framework for web attacks detection, mitigation and attacker profiling

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.829329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b1dd6cc3-c581-49ae-b5fd-f4896cfeb0fb · outbound

This paper cites Dynamic defenses and the transferability of adversarial examples.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Dynamic defenses and the transferability of adversarial examples

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.815714Z

Source-reported events for the cited work

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Observation 0d205af5-2248-494d-aadb-e86e4ebcd2e3 · outbound

This paper cites Predicting web vulnerabilities in web applications based on machine learning.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Predicting web vulnerabilities in web applications based on machine learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.802146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 936c8df5-76bf-4ace-a774-3adadd84b0d0 · outbound

This paper cites Learning web request patterns.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Learning web request patterns

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.788349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6aee187a-8da9-4478-935b-8a08c7900cd7 · outbound

This paper cites Text mining: open source tokenization tools-an analysis.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Text mining: open source tokenization tools-an analysis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.774511Z

Source-reported events for the cited work

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Observation 46694e4d-8bc4-49fc-aa28-95d90bc2e5aa · outbound

This paper cites Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility

Reference 39

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Observation 45852fea-82df-45b2-bbf0-066ebf45feb8 · outbound

This paper cites Attention is all you need.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Attention is all you need

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.760655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 91163643-2196-4568-b548-338c5d13606b · outbound

This paper cites Enhancing automatic modulation recognition for iot applications using transformers.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Enhancing automatic modulation recognition for iot applications using transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.746231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e83e40e2-014f-4bd3-9c16-d5e7a67626a4 · outbound

This paper cites Web application firewall using machine learning and features engineering.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Web application firewall using machine learning and features engineering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.729993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 770dd0db-3ef8-4874-a81f-976fe03b13de · outbound

This paper cites CNN Web Application Firewall.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders CNN Web Application Firewall

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.714998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a4d744fa-c454-40e8-a882-3f39bb1d83cf · outbound

This paper cites Detecting web attacks from HTTP weblogs using variational LSTM autoencoder deviation network.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Detecting web attacks from HTTP weblogs using variational LSTM autoencoder deviation network

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.701378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c339477-df82-45e2-af8e-05e654fa731c · outbound

This paper cites INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:40.473768Z

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Observation 7fd004f0-385f-42f1-80e5-b21a3d08ea57 · outbound

This paper cites A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:40.478485Z

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Observation 096da3b6-7c91-48dd-a52e-cedb2af1dbef · outbound

This paper cites Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:40.483761Z

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Observation 9c6e8dfb-8fb3-4459-a02e-43df60c329fb · outbound

This paper cites GenSQLi: A Generative Artificial Intelligence Framework for Automatically Securing Web Application Firewalls Against Structured Query Language Injection Attacks.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders GenSQLi: A Generative Artificial Intelligence Framework for Automatically Securing Web Application Firewalls Against Structured Query Language Injection Attacks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.686856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f8cba526-6e53-430b-887f-03092365f6d8 · outbound

This paper cites GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:59:40.580111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b776fcd-3624-4ab5-a008-62c0d5a06d5d · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:40.497690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0918c48-a86e-49a6-9c0a-8633076f47e6 · outbound

This paper cites Hybrid speech recognition with deep bidirectional LSTM.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Hybrid speech recognition with deep bidirectional LSTM

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:59:40.672576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8142da95-ca34-4fc2-b704-d1332b45f164 · outbound

This paper cites Reasoning with LLMs for Zero-Shot Vulnerability Detection.

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders Reasoning with LLMs for Zero-Shot Vulnerability Detection

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:40.506425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

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