The paper introduces GMA-SAWGAN-GP, a novel generative framework that significantly enhances Intrusion Detection System (IDS) performance by augmenting mixed-type network traffic data, especially impr…

The paper argues that zero-day attacks primarily exploit undisclosed vulnerabilities rather than exhibiting novel behaviors, advocating for vulnerability-centric detection methods over purely behavior…

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…

The paper proposes LARAR, a novel layer-wise adaptive regularization approach that enhances the adversarial robustness of neural network-based Network Intrusion Detection Systems by analyzing and miti…

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

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…

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…

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 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 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 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 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…

AEGIS introduces a novel physics-based system that analyzes encrypted network traffic flow dynamics, achieving state-of-the-art zero-day evasion detection with high accuracy and low latency.

The paper proposes a universal robustification framework to enhance drift-adaptive malware detectors against combined concept drift and adversarial attacks, significantly reducing attack success rates…

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…

FlowGuard introduces an identity-independent defense using flow matching to detect data-free model stealing attacks by identifying synthetic queries as out-of-distribution based on their lower-dimensi…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

This paper introduces a quantum optimization framework using QAOA to perform Subgroup Discovery for network intrusion detection, demonstrating that quantum methods can find complex feature interaction…

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…