Machine Learning-Enhanced Malware Obfuscation and Innovative Defense Strategies Article

Kanwal, Preet, Kumar, Tejas A, Sunil, Sanjay et al. (2026). Machine Learning-Enhanced Malware Obfuscation and Innovative Defense Strategies . IEEE ACCESS, 14 12605-12627. 10.1109/ACCESS.2026.3656242

Open Access International Collaboration

cited authors

  • Kanwal, Preet; Kumar, Tejas A; Sunil, Sanjay; Chandrasekaran, Srimanish; Jaiswal, Sajal; Honnavalli, Prasad B; Iyengar, SS

authors

publication date

  • January 1, 2026

published in

keywords

  • ACGAN (auxillary classifier GAN)
  • API (application programming interface)
  • Adaptation models
  • CLASSIFICATION
  • Computer Science
  • Computer Science, Information Systems
  • Encryption
  • Engineering
  • Engineering, Electrical & Electronic
  • Feature extraction
  • GENERATIVE ADVERSARIAL NETWORKS
  • Generative adversarial networks
  • Generators
  • Long short term memory
  • MALGAN (malware GAN)
  • MalCONV (malware convolutional network)
  • Malware
  • Operating systems
  • RECONSTRUCTION
  • Science & Technology
  • Static analysis
  • Technology
  • Telecommunications
  • Training
  • adversarial malware example (AME)
  • discriminator
  • generative adversarial network (GAN)
  • generator

Digital Object Identifier (DOI)

publisher

  • IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

start page

  • 12605

end page

  • 12627

volume

  • 14