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…

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…

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…

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 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…

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 introduces Dummy-Aware Weighted Attack (DAWA), a novel evaluation method that significantly reduces the reported robustness of Dummy Classes-based defenses by simultaneously targeting both t…

The paper proposes DFBScanner, a lightweight static parameter inspection framework that detects backdoor attacks by analyzing anomalous parameter updates in the final classification layer, achieving f…

The paper introduces 'adversarial restlessness,' an activation-level signature in LLM residual streams, to detect multi-turn prompt injection attacks with high accuracy.

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

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 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 SDNGuardStack, an explainable ensemble learning framework that achieves high-accuracy intrusion detection (99.98%) in Software-Defined Networks using the InSDN dataset.

The paper proposes a unified, architecture-agnostic framework that significantly improves the robustness of deepfake image detectors against adversarial attacks by focusing on higher-order frequency s…

The paper proposes Attack-AAIRS, a novel framework that uses GAN-generated synthetic adversarial samples to enhance the robustness of skeleton-based person identification models against unseen attacks…

The paper introduces GenTI, a novel LLM-driven benchmark and dataset, to automatically generate high-quality, deployable IDPS rules for detecting unseen and zero-day cyber attacks.

The paper proposes AEGIS, a novel diffusion-guided method for injecting adversarial perturbations into the latent space to create generalizable and robust defenses against advanced facial deepfake man…

The paper proposes an unsupervised method using multiple statistical indicators to detect adversarial or compromised context documents in Retrieval Augmented Generation (RAG) systems, even without kno…