TL;DR
SA-IDS is proposed, a self-supervised and adaptive intrusion detection framework designed for resource-constrained IIoT edge devices that leverages contrastive self-supervised learning to learn robust representations of benign telemetry data without requiring labeled attacks.
A hybrid IDS framework built on a stacking ensemble of four heterogeneous base classifiers, namely random forest, extreme gradient boosting, light gradient-boosting machine, and a shallow multi-layer perceptron (MLP), coupled with a PyTorch-based neural network meta-classifier, establishing that pairing meta-learning w...
Intrusion Detection Systems (IDSs) are essential for securing Internet of Things (IoT) and Internet of Medical Things (IoMT) environments, yet most machine learning-based IDSs assume that training and testing data follow similar distributions. In practice, domain shifts arising from differences in device characteristic...
A hybrid multi-layered intrusion detection framework combining traditional machine learning, Deep Neural Architectures (DenseNN), and ensemble methods to evaluate zero-day resilience within cloud-level backend connectivity interfacing EV and V2X management ecosystems is proposed.
Intrusion detection systems (IDS) are crucial for protecting the security of network devices. In a dynamic network environment, network attack behavior is constantly changing. IDS developed using closed datasets has limited detection capabilities and cannot effectively respond to potential unknown attacks. Existing met...
The rapid evolution of sophisticated cyber threats has drastically increased the cybersecurity risks in Industrial Internet of Things environments due to the massive interconnection of industrial devices, sensors, programmable logic controllers, gateways, and edge computing infrastructures. Traditional intrusion detect...
Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kin...
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