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…

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…

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…

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

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 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 introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

The paper demonstrates that simpler, shallower Deep Neural Network architectures with reduced features and ReLU activations can inherently improve the robustness of ML-NIDS against gradient-based adve…

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 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…

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

The paper proposes a confidence-aware, multi-layered Cloud-IDS pipeline that integrates adaptive Q-Learning, Chroma memory, and LLM semantic analysis to enhance detection accuracy and reduce reliance…

The paper proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

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…

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…

This paper proposes using a fine-tuned foundation model (MOMENT) to detect and classify various attacks in RPL-based IoT networks, achieving performance comparable to state-of-the-art methods.