This paper proposes a lightweight, machine learning-based model for on-device intrusion detection in resource-constrained IoT devices, achieving high detection accuracy for common cyber threats.

This multivocal literature review analyzes the convergence of IoT and Zero Trust security, finding that academia focuses on IoT modifications while industry prioritizes practical integration within ex…

The paper proposes EFAH-ZTM, an explainable federated framework that uses hypergraphs and autoencoders to perform robust zero-trust micro-segmentation in complex IIoT networks.

This survey reviews hardware-rooted trust mechanisms, such as PUFs and TPMs, demonstrating that hardware-based solutions are superior to software-only methods for ensuring secure authentication and AI…

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

This paper proposes an explainable threat attribution system for IoT networks that uses SHAP and flow behavior modeling to accurately classify and explain over 30 distinct attack variants into 8 meani…

This paper conducts a literature review of non-academic publications to consolidate current knowledge, trends, and future challenges regarding the industrial integration of IoT devices within a Zero T…

This paper evaluates a novel black-box adversarial attack to demonstrate the vulnerability of ML-based IoT Intrusion Detection Systems (IDS) and proposes a robust defense mechanism to mitigate these e…

The paper proposes a novel Federated Learning framework combined with Homomorphic Encryption and a dynamic agent selection scheme to enhance privacy and efficiency for anomaly detection in the Industr…

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

LiteAtt introduces a verifier-less, Peer-to-Peer Self-Attestation (P2P-SA) framework for modern IoT MCUs, enabling mutual authentication and firmware attestation directly within the connection handsha…

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

The paper introduces APIOT, the first LLM framework capable of autonomously performing the full discovery, exploitation, patching, and verification cycle against bare-metal industrial OT devices.

This paper proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…

MA-IDS proposes a Multi-Agent RAG framework that uses LLMs and a self-building Experience Library to achieve explainable and self-improving intrusion detection for resource-constrained IoT networks.

The study assesses the generalization capability of supervised machine learning models for intrusion detection using UNSW-NB15 and TON_IoT, finding a significant performance drop when models are teste…

The paper proposes a trust-aware federated hybrid intrusion detection framework using multiple ML models at distributed edge nodes to proactively secure highly connected Intelligent Transport Systems.

A hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attention achieves high accuracy and low latency for robust cyberattack detection in Industrial IoT environments.

This paper experimentally demonstrates the high detection performance of machine learning-based intrusion detection systems for identifying cyberattacks targeting OPC UA applications running over priv…

This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) that effectively detects diverse cyberattacks in IoMT networks, achieving high accuracy and providing enhanced i…