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…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

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…

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.

The paper proposes an SE ViT-BiLSTM hybrid model for enhanced intrusion detection in IIoT and IoMT environments, achieving superior performance on real-world datasets, especially after data balancing.

DSTAN-Med is a novel dual-channel attention framework that significantly improves False Data Injection (FDI) attack detection in IoMT medical devices by explicitly separating spatial and temporal depe…

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

This paper analyzes how vulnerable various machine learning models are to data poisoning attacks in IoT intrusion detection, finding that ensemble methods are more robust than Logistic Regression and…

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.

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…

The paper introduces GenTI, a novel LLM-driven benchmark and dataset, to automatically generate high-quality, deployable IDPS rules for detecting unseen and zero-day cyber attacks.

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…

The paper proposes a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

The paper introduces SAMD, an automated tool that uses STPA-Sec to identify potential false data injection attack scenarios in AI/ML-enabled medical devices during the design phase.

This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

This paper proposes and evaluates two lightweight deep learning-based intelligent Intrusion Detection Systems (CNN and LSTM) to enhance the security of large-scale IoT networks, achieving high classif…

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.

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.

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…