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Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning

Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning

8 June 2023
Mohamed el Shehaby
Ashraf Matrawy
    AAML
ArXivPDFHTML

Papers citing "Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning"

4 / 4 papers shown
Title
Introducing Perturb-ability Score (PS) to Enhance Robustness Against Problem-Space Evasion Adversarial Attacks on Flow-based ML-NIDS
Introducing Perturb-ability Score (PS) to Enhance Robustness Against Problem-Space Evasion Adversarial Attacks on Flow-based ML-NIDS
Mohamed elShehaby
Ashraf Matrawy
AAML
31
0
0
11 Sep 2024
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance
  ML Robustness
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
Mohamed el Shehaby
Aditya Kotha
Ashraf Matrawy
AAML
23
0
0
15 Mar 2024
Adversarial Machine Learning In Network Intrusion Detection Domain: A
  Systematic Review
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review
Huda Ali Alatwi
C. Morisset
AAML
27
23
0
06 Dec 2021
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,837
0
08 Jul 2016
1