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 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 proposes a comprehensive framework for network intrusion detection using unified multi-modal datasets and evaluates advanced adversarial learning methods for generating high-fidelity synthe…

The paper empirically evaluates domain-adapted and general-purpose LLMs for structured threat modelling (STRIDE on 5G security), finding that domain adaptation and model size do not guarantee reliable…

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…

The paper proposes DAIRE, a lightweight AI model, for highly efficient, real-time detection and classification of various cyberattacks targeting the vulnerable Controller Area Network (CAN) in the Int…

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

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 evaluates unsupervised temporal learning models, specifically recurrent autoencoders, for real-time anomaly detection in vulnerable IEC-61850 GOOSE networks, demonstrating that the GRU mode…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

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…

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

This paper compares lightweight machine learning models (like Random Forest) against computationally intensive deep learning methods for botnet detection on the CTU-13 dataset, showing that these simp…

The paper introduces RoboKA, a KAN-based multimodal fusion framework, and a synthetic dataset (Robo-SAr) to significantly improve robocall surveillance by modeling complex adversarial interactions bet…

This paper analyzes the latency-accuracy trade-offs of various TinyML models for detecting diverse cyber-RF threats on autonomous spacecraft, finding that Logistic Regression offers an effective, low-…

This paper proposes a gap-prioritization framework to bridge the gap between theoretical cyber attack prediction research and practical operational deployment by identifying critical implementation hu…

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…