Boosting Industrial Internet of Things Intrusion Detection - Leveraging Machine Learning and Feature Selection Techniques - Machine Learning

Aug 5, 2026

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Rapid adoption of industrial internet of things (IIoT) technologies in Industry 4.0 improves efficiency and automation while creating significant cybersecurity vulnerabilities. This work evaluates machine learning (ML) classifiers for anomaly detection in IIoT settings and introduces feature selection to raise both accuracy and computational efficiency. Results show feature selection increases detection performance and reduces resource demand, supporting real-time deployment of resilient intrusion detection systems (IDS). Insights inform stronger security frameworks for protecting IIoT reliability, integrity, and safety.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Industry 4.0 and IIoT connectivity

Cybersecurity challenges in IIoT

Objectives and approach: ML classifiers and feature selection

Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques Lahcen Idouglid, Said Tkatek, Khalid Elfayq Faculty of Sciences, Computer Sciences Research Laboratory, Ibn Tofail University, Kenitra, Morocco | Article history:
Received Aug 14, 2024 Revised Oct 31, 2024 Accepted Nov 14, 2024 | | --- | | Keywords:
Anomaly detection Feature selection
Industrial internet of things security
Industry 4.0 Intrusion detection Machine learning | Corresponding Author: The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings. This is an open access article under the CC BY-SA license. Lahcen Idouglid Faculty of Sciences, Computer Sciences Research Laboratory, Ibn Tofail University Kenitra, Morocco Email: [lahcen.idouglid@uit.ac.ma](lahcen.idouglid@uit.ac.ma) Article Info ABSTRACT 1. INTRODUCTION Industry 4.0, known as the fourth industrial revolution, represents a major shift in manufacturing processes by integrating cyber-physical systems, automation, and smart technologies. A key component of Industry 4.0 is the industrial internet of things (IIoT), which connects machines, devices, and systems within industrial environments through advanced communication networks. This connectivity enables real-time data collection, analysis, and decision-making, significantly improving efficiency, productivity, and flexibility in manufacturing processes [1] . The IIoT plays a pivotal role in enhancing operational capabilities by facilitating seamless information exchange between machines and systems. Its adoption allows industries to optimize manufacturing processes, predict maintenance needs, and develop smart factories that operate autonomously and adaptively. This has led to widespread adoption of IIoT technologies globally, giving industries a competitive edge [2] . However, the rapid implementation of IIoT introduces critical cybersecurity challenges. The interconnected nature of these systems makes them vulnerable to cyber threats, which can lead to operational disruptions, financial losses, and compromised sensitive data. Ensuring the security of IIoT environments has thus become a top priority for both industry leaders and researchers as the reliance on Industry 4.0 technologies grows [3] . As the IIoT becomes increasingly integrated into Industry 4.0, its cybersecurity becomes even more essential. IIoT systems, which connect a vast array of devices, sensors, and machinery, are crucial for the efficiency of modern industrial operations. However, their interconnectedness introduces significant vulnerabilities that can be exploited by cybercriminals. The disruption of IIoT networks through cyberattacks can result in production downtimes, financial losses, and threats to human safety [4] . The characteristics of IIoT environments, such as their scale, heterogeneity, and real-time operations, make them susceptible to a range of cyber threats, including distributed denial of service (DDoS) attacks, data breaches, and manipulation of critical processes. Additionally, the use of legacy systems with li

FAQs

What problem does the study address in Industry 4.0 IIoT?

It addresses critical cybersecurity vulnerabilities created by integrating IIoT technologies into Industry 4.0, which expose industrial systems to cyber threats and potential operational disruption.

How does the paper improve intrusion detection performance?

It evaluates machine learning classifiers for anomaly detection and applies feature selection techniques to improve accuracy while reducing computational demands.

Why are ML-based intrusion detection systems important for IIoT?

ML-based IDS can learn patterns from historical data and detect both known and unknown threats, making them suitable for identifying novel attacks and anomalies in IIoT networks.

Aug 5, 2026

Upload by Miles