Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs | Paper Citation Record

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This paper cites Using machine learning for healthcare challenges and opportunities.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Using machine learning for healthcare challenges and opportunities

Reference 1

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This paper cites Machine learning in healthcare.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Machine learning in healthcare

Reference 2

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This paper cites Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology

Reference 3

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Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Deep neural networks in healthcare systems

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Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Unresolved cited work

Reference 5

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This paper cites Autonomous driving ar- chitectures: insights of machine learning and deep learning algorithms.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Autonomous driving ar- chitectures: insights of machine learning and deep learning algorithms

Reference 6

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This paper cites Exploring the mechanism of crashes with autonomous vehi- cles using machine learning.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Exploring the mechanism of crashes with autonomous vehi- cles using machine learning

Reference 7

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This paper cites Deep neural networks with koopman operators for modeling and control of autonomous vehicles.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Deep neural networks with koopman operators for modeling and control of autonomous vehicles

Reference 8

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This paper cites Simple black- box adversarial attacks on deep neural networks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Simple black- box adversarial attacks on deep neural networks

Reference 9

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This paper cites Adversarial attacks on deep neural networks for time series classification.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial attacks on deep neural networks for time series classification

Reference 10

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This paper cites Fast adversarial attacks to deep neural networks through gradual sparsifica- tion.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Fast adversarial attacks to deep neural networks through gradual sparsifica- tion

Reference 11

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Observation 2413acd9-57c8-49a5-9c68-68a0be8c2cd6 · outbound

This paper cites Adversarial Machine Learning at Scale.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial Machine Learning at Scale

Reference 12

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This paper cites Are self-driving cars secure? evasion attacks against deep neural networks for steering angle prediction.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Are self-driving cars secure? evasion attacks against deep neural networks for steering angle prediction

Reference 13

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This paper cites Real-time evasion attacks against deep learning-based anomaly detection from distributed system logs.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Real-time evasion attacks against deep learning-based anomaly detection from distributed system logs

Reference 14

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This paper cites What is adversarial machine learning? attack methods in 2024, June 2024.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs What is adversarial machine learning? attack methods in 2024, June 2024

Reference 15

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This paper cites Evasion attacks against machine-learning based behavioral authentication.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Evasion attacks against machine-learning based behavioral authentication

Reference 16

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Observation ed8f5ceb-2f2f-4275-bd25-19fc9a3535a7 · outbound

This paper cites Intriguing properties of neural networks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Intriguing properties of neural networks

Reference 17

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This paper cites Explaining and Harnessing Adversarial Examples.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Explaining and Harnessing Adversarial Examples

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This paper cites Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms

Reference 19

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This paper cites Adversarial training against adversarial attacks for machine learning-based intrusion detection systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial training against adversarial attacks for machine learning-based intrusion detection systems

Reference 20

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Observation aca55bf3-ddb5-4a48-9bd0-0612c87a31a0 · outbound

This paper cites Defending evasion attacks via adversarially adaptive training.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Defending evasion attacks via adversarially adaptive training

Reference 21

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This paper cites Adversarial training methods for deep learning: A systematic review.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial training methods for deep learning: A systematic review

Reference 22

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Observation cfdb93f5-ea43-49a5-9c02-8b0c0c6618d1 · outbound

This paper cites Feature denoising for improving adversarial robustness.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Feature denoising for improving adversarial robustness

Reference 23

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This paper cites Evaluating and improving ad- versarial robustness of machine learning-based network intrusion detec- tors.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Evaluating and improving ad- versarial robustness of machine learning-based network intrusion detec- tors

Reference 24

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This paper cites Adversarial feature alignment: Balancing robustness and accuracy in deep learning via adversarial training, 2024.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial feature alignment: Balancing robustness and accuracy in deep learning via adversarial training, 2024

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This paper cites The more the merrier: adding hidden measurements to secure industrial control systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs The more the merrier: adding hidden measurements to secure industrial control systems

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This paper cites Adversarial Regression for Detecting Attacks in Cyber-Physical Systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial Regression for Detecting Attacks in Cyber-Physical Systems

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This paper cites Lunar: Unifying local outlier detection methods via graph neural networks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Lunar: Unifying local outlier detection methods via graph neural networks

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This paper cites Robustness test- ing of data and knowledge driven anomaly detection in cyber-physical systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Robustness test- ing of data and knowledge driven anomaly detection in cyber-physical systems

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This paper cites Adversarial attack mitigation strategy for machine learning-based network attack detection model in power system.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial attack mitigation strategy for machine learning-based network attack detection model in power system

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This paper cites Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks

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This paper cites Deep reinforcement learning based evasion generative adversarial network for botnet detection.Future Generation Computer Systems , 150:294–302, 2024.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Deep reinforcement learning based evasion generative adversarial network for botnet detection.Future Generation Computer Systems , 150:294–302, 2024

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This paper cites Strengthening ids against evasion attacks with gan-based adversarial samples in sdn-enabled network.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Strengthening ids against evasion attacks with gan-based adversarial samples in sdn-enabled network

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This paper cites Omni: Au- tomated ensemble with unexpected models against adversarial evasion attack.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Omni: Au- tomated ensemble with unexpected models against adversarial evasion attack

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This paper cites Clustering and ensemble based approach for securing electricity theft detectors against evasion attacks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Clustering and ensemble based approach for securing electricity theft detectors against evasion attacks

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This paper cites Mitigating adversarial evasion attacks of ransomware using ensemble learning.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Mitigating adversarial evasion attacks of ransomware using ensemble learning

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This paper cites Evasion attack and defense on machine learn- ing models in cyber-physical systems: A survey.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Evasion attack and defense on machine learn- ing models in cyber-physical systems: A survey

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This paper cites Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

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This paper cites Evaluating the adversarial robustness of adaptive test-time defenses.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Evaluating the adversarial robustness of adaptive test-time defenses

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This paper cites Adversarial attacks to machine learning-based smart healthcare systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial attacks to machine learning-based smart healthcare systems

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Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Unresolved cited work

Reference 41

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This paper cites Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks

Reference 42

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This paper cites Dealing with noise problem in machine learning data-sets: A systematic review.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Dealing with noise problem in machine learning data-sets: A systematic review

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This paper cites Adversarial learning with cost-sensitive classes.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial learning with cost-sensitive classes

Reference 44

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This paper cites Adversarial training for privacy-preserving deep learning model distribution.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial training for privacy-preserving deep learning model distribution

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Observation 579ab869-79db-4238-b200-ce4bb8a178f7 · outbound

This paper cites Robustness, Privacy, and Generalization of Adversarial Training.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Robustness, Privacy, and Generalization of Adversarial Training

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This paper cites The ohiot1dm dataset for blood glucose level prediction: Update 2020.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs The ohiot1dm dataset for blood glucose level prediction: Update 2020

Reference 47

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This paper cites Deep Residual Time- Series Forecasting: Application to Blood Glucose Prediction.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Deep Residual Time- Series Forecasting: Application to Blood Glucose Prediction

Reference 48

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Observation 73c5c0c9-fae2-4fba-ab12-d0e35c4676fa · outbound

This paper cites Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems

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Observation 720dedac-1241-4c09-984a-04951caea913 · outbound

This paper cites BLURtooth: Exploiting Cross- Transport Key Derivation in Bluetooth Classic and Bluetooth Low Energy.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs BLURtooth: Exploiting Cross- Transport Key Derivation in Bluetooth Classic and Bluetooth Low Energy

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.803438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa0a9bbf-48af-40bc-89f7-0f4085c9f524 · outbound

This paper cites Medtronic MyCareLink Smart Vulnerability.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Medtronic MyCareLink Smart Vulnerability

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.792082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74403d03-6778-4812-a2a9-593b7b6a5047 · outbound

This paper cites Securing automated insulin delivery systems: A review of security threats and protectives strategies.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Securing automated insulin delivery systems: A review of security threats and protectives strategies

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:34.232957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 996737a9-418a-4593-aa97-ecc90dec1b17 · outbound

This paper cites Com- partmentation policies for android apps: A combinatorial optimization approach.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Com- partmentation policies for android apps: A combinatorial optimization approach

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.780854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ea88c53-489b-4216-8084-454c501f6e71 · outbound

This paper cites an unresolved cited work.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:34.769852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60068f3a-fa61-40a5-aa0c-41301ca8dd01 · outbound

This paper cites URET: Universal Robustness Evaluation Toolkit (for Evasion).

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs URET: Universal Robustness Evaluation Toolkit (for Evasion)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.759034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb472c50-6ee6-4712-891f-93bc0809935d · outbound

This paper cites A review of anomaly detection techniques based on nearest neighbor.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs A review of anomaly detection techniques based on nearest neighbor

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.747280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 267f2dce-a048-4b73-b7c3-950ac86f2c53 · outbound

This paper cites Sparse coding with anomaly detection.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Sparse coding with anomaly detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.736134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8324c582-7983-47fa-a8b3-b04bb2564616 · outbound

This paper cites Detection and explanation of anomalies in healthcare data.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Detection and explanation of anomalies in healthcare data

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.724436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21d78e3d-0606-45f2-9664-197a0c821d59 · outbound

This paper cites Sparsity techniques in medical imaging.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Sparsity techniques in medical imaging

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.712698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e8b7bb8-9f27-4afe-8650-290888ebe519 · outbound

This paper cites Improv- ing one-class svm for anomaly detection.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Improv- ing one-class svm for anomaly detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.701662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c478788b-b3a6-472f-acbe-9f358e8478f6 · outbound

This paper cites Analyzing the non-linear relationship between fasting blood glucose levels and gensini score in patients with stemi.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Analyzing the non-linear relationship between fasting blood glucose levels and gensini score in patients with stemi

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.688816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c81e759-7354-4293-9fc5-6be01e196045 · outbound

This paper cites Nonlinear metabolic effect of insulin across the blood glucose range in patients with type 1 diabetes mellitus.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Nonlinear metabolic effect of insulin across the blood glucose range in patients with type 1 diabetes mellitus

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.676409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 013cbeb9-48bc-4381-8c5b-b59c7c5b600f · outbound

This paper cites Somogyi effect: What it is, causes, symp- toms & treatment.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Somogyi effect: What it is, causes, symp- toms & treatment

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.664515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 927e152d-4f89-4d82-8a3a-8c8441fe398c · outbound

This paper cites Hypoglycemia.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Hypoglycemia

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.652044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb10025c-3b41-411b-b3d3-1cd8f4ea487c · outbound

This paper cites an unresolved cited work.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:34.638383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e16dcb51-3e1d-45e7-ad53-fc4c07a3b53e · outbound

This paper cites Hypoglycemia (low blood sugar), February 2024.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Hypoglycemia (low blood sugar), February 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.626052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 149b552c-bf11-4b92-8d38-c94a94684021 · outbound

This paper cites What is hierarchical clustering?, August 2024.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs What is hierarchical clustering?, August 2024

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:34.613530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 777d55d9-563f-4b22-b09d-3b41ac72d596 · outbound

This paper cites Patient i.

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Patient i

Reference 68

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:45:34.601008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers