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. 2505.01437
  4. Cited By
Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets

Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets

26 April 2025
Hassan Wasswa
Timothy Lynar
Hussein Abbass
ArXivPDFHTML

Papers citing "Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets"

3 / 3 papers shown
Title
Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning
Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning
Hassan Wasswa
Aziida Nanyonga
Timothy Lynar
21
1
0
26 Apr 2025
IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic
IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic
Hassan Wasswa
Timothy Lynar
Aziida Nanyonga
Hussein Abbass
52
1
0
26 Apr 2025
Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder
Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder
Hassan Wasswa
Aziida Nanyonga
Timothy Lynar
DRL
47
2
0
21 Apr 2025
1