Cybersecurity Challenges and AI-Based Solutions in Industry 4.0 Environments
Year: 2026
Type: conference-paper
Abstract: Industry 4.0 integrates technologies such as the Industrial Internet of Things (IIoT), cyber-physical systems (CPS), cloud computing, big data analytics, and automation to create smart, interconnected manufacturing environments. These advancements significantly enhance efficiency, flexibility, and productivity. However, the increased connectivity and heterogeneity of devices expand the attack surface for cyber threats. Cyberattacks can disrupt operations, compromise sensitive data, and even pose risks to human safety in industrial settings. Traditional rule-based defenses (e.g., firewalls, signature-based intrusion detection systems) are insufficient against sophisticated threats like zero-day exploits and advanced persistent threats (APTs). This paper comprehensively analyzes key cybersecurity challenges in Industry 4.0, such as IIoT vulnerabilities, legacy system integration issues, insider threats, supply chain attacks, and real-time operational constraints. We propose a layered AI-based security framework that leverages machine learning for proactive threat detection, anomaly identification, and automated response. The framework is evaluated experimentally using the NSL-KDD dataset, demonstrating superior performance of Random Forest over Logistic Regression in terms of accuracy, precision, recall, and F1-score. Results confirm that AI-driven approaches substantially improve detection rates and reduce false positives, thereby strengthening cybersecurity resilience in Industry 4.0 environments. The contributions of this work include a detailed examination of challenges, a novel architecture design, empirical validation, and recommendations for future enhancements. This study is intended to guide researchers and practitioners in developing robust security solutions for modern industrial ecosystems.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7149928237