Research Article
Assessment of Zero-Day Vulnerability using Machine Learning Approach
@ARTICLE{10.4108/eetiot.4978, author={SakthiMurugan S and Sanjay Kumaar A and Vishnu Vignesh and Santhi P}, title={Assessment of Zero-Day Vulnerability using Machine Learning Approach}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={1}, keywords={zero-day vulnerabilities, machine learning, autoencoder model, neural network, intrusion detection}, doi={10.4108/eetiot.4978} }- SakthiMurugan S
Sanjay Kumaar A
Vishnu Vignesh
Santhi P
Year: 2024
Assessment of Zero-Day Vulnerability using Machine Learning Approach
IOT
EAI
DOI: 10.4108/eetiot.4978
Abstract
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.
Copyright © 2024 SakthiMurugan S et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
