Intelligence at the Edge: AI/ML for IoT Systems

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue "Intelligence at the Edge: AI/ML for IoT Systems".

The rapid proliferation of Internet of Things (IoT) devices has pushed computational intelligence from centralized cloud infrastructures toward the network edge, creating both unprecedented opportunities and significant security challenges. While edge computing enables low-latency, bandwidth-efficient, and privacy-preserving data processing, the distributed and resource-constrained nature of edge IoT systems makes them inherently vulnerable to a wide range of cyber threats. Traditional security mechanisms—such as signature-based intrusion detection, rule-based monitoring, and manual penetration testing—are often ill-suited for dynamic, large-scale edge environments where heterogeneity, limited compute power, and real-time constraints prevail.

This Special Issue focuses on the transformative role of Artificial Intelligence and Machine Learning (AI/ML) in securing next-generation IoT systems at the edge. We seek contributions that integrate classical security techniques (e.g., pentesting, reverse engineering, anomaly detection) with modern AI-driven approaches for attack detection, threat modeling, and automated response, all adapted to the unique constraints of edge devices. Special emphasis is placed on lightweight and explainable AI (XAI) models that provide transparent, interpretable decisions—critical for building trust in autonomous edge security operations.

We welcome original research articles, comprehensive reviews, and case studies that advance the state of the art in intelligent, edge-native security for IoT systems.

Selected topics include (but are not limited to) the following:

• AI/ML-enhanced security for edge IoT systems;
• Lightweight intrusion detection systems (IDSs) for resource-constrained edge devices;
• Edge-based zero-day threat detection and analysis;
• Traditional security techniques (pentesting, signature recognition, rule-based detection, reverse engineering) adapted to edge IoT;
• Security lifecycle management from detection to response at the edge;
• IDS training and datasets for edge/IoT environments;
• Attack execution analysis and threat modeling for distributed edge networks;
• Explainable AI (XAI) for transparent edge security decisions;
• Trustworthy and interpretable AI models for autonomous IoT security;
• Automated decision-making in edge-based cybersecurity;
• AI-driven threat intelligence and incident response at the edge;
• Adversarial machine learning and robust AI defenses for edge IoT;
• Human–AI collaboration in edge security operations;
• Federated learning and privacy-preserving AI for distributed edge security;
• Energy-efficient AI/ML models for always-on edge protection.

Dr. Sanjoy Paul
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• edge intelligence for IoT
• AI/ML at the network edge
• lightweight intrusion detection
• IoT security architectures
• zero-day attack detection in edge networks
• explainable AI (XAI) for edge systems
• trustworthy edge AI
• automated security response
• adversarial machine learning for IoT
• real-time threat mitigation
• federated learning for edge security
• resource-aware AI models
• edge-based anomaly detection
• privacy-preserving edge computing
• human-in-the-loop edge security