Assessment of Zero-Day Vulnerability using Machine Learning Approach


  • SakthiMurugan S Amrita School of Computing
  • Sanjay Kumaar A Amrita School of Computing
  • Vishnu Vignesh Amrita School of Computing
  • Santhi P Amrita School of Computing



zero-day vulnerabilities, machine learning, autoencoder model, neural network, intrusion detection


Organisations and people are seriously threatened by zero-day vulnerabilities because they may be utilised by attackers to infiltrate systems and steal private data. Currently, Machine Learning (ML) techniques are crucial for finding zero-day vulnerabilities since they can analyse huge datasets and find patterns that can point to a vulnerability. This research’s goal is to provide a reliable technique for detecting intruders and zero-day vulnerabilities in software systems. The suggested method employs a Deep Learning (DL) model and an auto-encoder model to find unusual data patterns. Additionally, a model for outlier detection that contrasts the autoencoder model with the single class-based Support Vector Machine (SVM) technique will be developed. The dataset of known vulnerabilities and intrusion attempts will be used to train and assess the models.


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How to Cite

S. S, S. K. A, V. Vignesh, and S. P, “Assessment of Zero-Day Vulnerability using Machine Learning Approach”, EAI Endorsed Trans IoT, vol. 10, Jan. 2024.