AI-Powered Cybersecurity Models for Training and Testing IoT Devices
Source abstract
Abstract
The rapid expansion of the Internet of Things (IoT) has introduced a significant attack surface, making robust security solutions essential. Traditional signature-based methods are often inadequate against modern, versatile threats. Consequently, research has shifted towards AI and machine learning models for intrusion detection. However, much of the existing research suffers from ‘siloed evaluation,’ where models are trained and tested on single, often outdated datasets, leading to poor generalization in real-world, diverse environments. This paper addresses this critical gap by presenting a comprehensive benchmark of leading AI-powered models across a suite of contemporary IoT cybersecurity datasets from the Canadian Institute of Cybersecurity (CIC). We evaluate a range of machine learning and deep learning algorithms, focusing on their detection performance, cross-dataset generalization, and robustness to provide a realistic assessment of their capabilities for securing modern IoT ecosystems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · Adversarial Robustness in Machine Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Cybersecurity and Forensics Education (C^{2}SAFE), Illinois Institute of Technology, 3300 S Federal St., Chicago, IL 60616, USA
Funding
Dr Maurice Dawson
References (32)
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