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…

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 introduces FIRCE, a framework that enhances intrusion detection systems by combining conformal evaluation for uncertainty quantification and drift detection with an adaptive chunking mechani…

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…

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 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 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 systematically evaluates various tabular representation learning techniques to automatically extract robust features from NetFlow data for network intrusion detection, finding that supervise…

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.

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…

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 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 introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…

The paper introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…

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…

The paper proposes PROVFUSION, a multi-view fusion framework that integrates anomaly signals from attribute, structure, and causality views to overcome the limitations of single node- or edge-centric…

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 a novel byte-level method to encode network flow records into fixed-size RGB images, significantly improving the performance of Intrusion Detection Systems (IDS) by allowing convo…

GESR introduces a graph-based framework that reconstructs edge semantics from local structural context to detect stealthy malicious communications using only benign training data, achieving high perfo…