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 evaluates AI's effectiveness in detecting network intrusions and cryptographic side-channel leakage, finding high accuracy in stable environments but performance degradation with novel traff…

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 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 anomaly-based Intrusion Detection Systems by integrating process mining to provide detailed, process-based explanations and severity ratings for detected network anomalies.

UNAD+ is an enhanced, explainable hybrid framework that effectively detects unknown zero-day network attacks by combining unsupervised ensemble methods with supervised refinement and post hoc explaina…

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…

ML Defender (aRGus NDR) is an open-source, embedded Machine Learning Network Intrusion Detection System (NIDS) that achieves superior detection rates for botnet and anomalous traffic on resource-const…

This Survey of Knowledge (SoK) identifies a disconnect between academic NIDS research and real-world operational contexts, proposing foundational changes to reshape future research.

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…

SentinelSphere is an AI platform that integrates advanced deep learning for real-time threat detection with an LLM-powered training system to holistically address both technical and human-factor cyber…

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

The paper proposes a dynamic risk assessment framework that combines Bayesian Attack Graphs (BAGs) with process mining to continuously monitor system behavior and update the probability of active vuln…

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 paper introduces ESPRESSO, a deep learning model that significantly improves the detection of sophisticated stepping-stone intrusions by correlating network flows across multiple relay hosts.

The paper proposes a lightweight hybrid MLP framework that uses structural URL features to achieve highly accurate and computationally efficient real-time phishing URL detection, outperforming several…

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 proposes an enhanced Wasserstein GAN with Gradient Penalty (SA-JS-WGAN-GP) incorporating Self-Attention and Jensen-Shannon Divergence to synthesize diverse network traffic data, significantl…