Download PDFOpen PDF in browser

Demystifying Deep Learning: Transparent Approaches and Visual Insights for Image Analysis

EasyChair Preprint no. 12040

11 pagesDate: February 12, 2024


The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience, but it has also opened new avenues for security threats. Intrusion detection plays a crucial role in safeguarding IoT networks from malicious activities. This paper explores the integration of machine learning strategies to enhance intrusion detection in connected networks. We investigate the challenges posed by the dynamic and heterogeneous nature of IoT environments and propose advanced machine learning approaches to address these challenges. The effectiveness of the proposed strategies is evaluated through comprehensive simulations, demonstrating their potential to significantly improve the security posture of IoT networks.

Keyphrases: Convolutional Neural Networks (CNNs), deep learning, Explainable AI, Feature Attribution, image recognition, interpretability, Model Explainability, neural networks, Transparent Models, Visualization techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Usman Hider},
  title = {Demystifying Deep Learning: Transparent Approaches and Visual Insights for Image Analysis},
  howpublished = {EasyChair Preprint no. 12040},

  year = {EasyChair, 2024}}
Download PDFOpen PDF in browser