Analysis of Approaches to Detecting Zero-Day Attacks in Internet of Things Networks
Abstract
Analysis of Approaches to Detecting Zero-Day Attacks in Internet of Things Networks
Incoming article date: 17.03.2025Malicious actors often exploit undetected vulnerabilities in systems to carry out zero-day attacks. Existing traditional detection systems, based on deep learning and machine learning methods, are not effective at handling new zero-day attacks. These attacks often remain incorrectly classified, as they represent new and previously unknown threats. The expansion of the Internet of Things (IoT) networks only contributes to the increase in such attacks. This work analyzes approaches capable of detecting zero-day attacks in IoT networks, based on an unsupervised approach that does not require prior knowledge of the attacks or the need to train intrusion detection systems (IDS) on pre-labeled data.
Keywords: Internet of Things, zero-day attack, autoencoder, machine learning, neural network, network traffic