ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1909.04778
  4. Cited By
Effectiveness of Adversarial Examples and Defenses for Malware
  Classification

Effectiveness of Adversarial Examples and Defenses for Malware Classification

10 September 2019
Robert Podschwadt
Hassan Takabi
    AAML
ArXiv (abs)PDFHTML

Papers citing "Effectiveness of Adversarial Examples and Defenses for Malware Classification"

4 / 4 papers shown
Title
Effectiveness of Moving Target Defenses for Adversarial Attacks in
  ML-based Malware Detection
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
Aqib Rashid
Jose Such
AAML
48
2
0
01 Feb 2023
Deep Learning for Android Malware Defenses: a Systematic Literature
  Review
Deep Learning for Android Malware Defenses: a Systematic Literature Review
Yue Liu
Chakkrit Tantithamthavorn
Li Li
Yepang Liu
AAML
81
81
0
09 Mar 2021
Sparse-RS: a versatile framework for query-efficient sparse black-box
  adversarial attacks
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
Francesco Croce
Maksym Andriushchenko
Naman D. Singh
Nicolas Flammarion
Matthias Hein
105
101
0
23 Jun 2020
MAB-Malware: A Reinforcement Learning Framework for Attacking Static
  Malware Classifiers
MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
Wei Song
Xuezixiang Li
Sadia Afroz
D. Garg
Dmitry Kuznetsov
Heng Yin
AAML
117
27
0
06 Mar 2020
1