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.

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

This paper proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…

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 enhances an existing autonomous online Intrusion Detection System (AOC-IDS) for IoT by addressing class imbalance, pseudo-label reliability, and computational overhead, achieving significan…

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…

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 improves IoT intrusion detection by addressing severe class imbalance using SMOTE and evaluating eight machine learning models, finding that Random Forest and Extra Trees achieve high perfo…

This paper improves IoT intrusion detection by addressing severe class imbalance using SMOTE and comparing the performance of multiple machine learning models on side-channel power data, showing Rando…

The paper introduces BRIDGE, a standardized benchmark for cross-domain IoT botnet detection, and TCH-Net, a novel multi-branch network that achieves state-of-the-art generalization performance across…

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…

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 paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

A-THENA is a lightweight, Transformer-based early intrusion detection system that significantly improves IoT security by incorporating time-aware hybrid encoding and network-specific augmentation, ach…

The paper proposes CANGuard, a hybrid CNN-GRU-Attention deep learning model, to accurately detect sophisticated Denial-of-Service and spoofing attacks targeting critical in-vehicle CAN bus networks.

NetVAD proposes a novel, identifier-free Variational Autoencoder that leverages frozen Foundation Models to achieve highly competitive unsupervised performance for zero-day intrusion detection.

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 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…