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Adversarial Machine Learning

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Influence Based Defense Against Data Poisoning Attacks in Online Learning

Submitted by grigby1 on Thu, 06/22/2023 - 3:13pm

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A Robust Framework for Adaptive Selection of Filter Ensembles to Detect Adversarial Inputs

Submitted by grigby1 on Fri, 01/06/2023 - 3:25pm

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Detection and Mitigation of Targeted Data Poisoning Attacks in Federated Learning

Submitted by grigby1 on Fri, 01/06/2023 - 3:25pm

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FALIoTSE: Towards Federated Adversarial Learning for IoT Search Engine Resiliency

Submitted by grigby1 on Fri, 11/18/2022 - 4:43pm

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A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems

Submitted by grigby1 on Tue, 09/20/2022 - 1:13pm

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Detection of Induced False Negatives in Malware Samples

Submitted by grigby1 on Tue, 09/20/2022 - 1:12pm

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Adversarial Attacks to API Recommender Systems: Time to Wake Up and Smell the Coffee?

Submitted by grigby1 on Fri, 07/15/2022 - 11:19am

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SaTC: CORE: Medium: Hidden Rules in Neural Networks as Attacks and Adversarial Defenses

Submitted by JasonGigax on Wed, 06/22/2022 - 2:31pm. Contributors:

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Using Deep Reinforcement Learning to Evade Web Application Firewalls

Submitted by grigby1 on Tue, 04/19/2022 - 12:17pm

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