Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application

Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application

In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions...

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Bibliographic Details
Main Authors: Tommaso Zoppi, Andrea Ceccarelli, Andrea Bondavalli
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Zero-day attacks
intrusion detection
machine learning
anomaly detection
RELOAD
security
Online Access:https://ieeexplore.ieee.org/document/9461213/

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