Papers Network Intrusion Detection
“Network Intrusion Detection” 태그가 달린 논문 328편 · 필터 해제
From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks is mature, existing systematic reviews suffe…
Network Intrusion DetectionRobust Unsupervised Network Intrusion Detection via Federated Learning with Selective Aggregation under Anomalous Sample Contamination
Network intrusion detection systems (NIDS) have become essential for Internet of Things (IoT) environments, as malware targeting IoT devices continues to evolve in sophistication. Unsupervised learning approaches offer a…
Network Intrusion DetectionFederated LearningAnomaly DetectionHarnessing Disagreement: Detecting Correlated Agreement Blindness in Multi-Agent Triage
Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety monitoring where correlated failures concentrate. We term…
Network Intrusion DetectionEnhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns. Unlike general IT …
Network Intrusion DetectionMulti-Level Distributional Entropy for Explainable Network Intrusion Detection
Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggre…
Network Intrusion DetectionDecoherence as Defence and the Magnitude of Noise Regularisation: A Rigorous N -Qubit Theory of Stochastic Quantum Neural Networks for Adversarially Robust Network Intrusion Detection
Stochastic quantum neural networks (SQNNs) encode neuronal activations as qubits, synaptic topology as entanglement, and neural noise through a Lindblad master equation. A recent conference study applied a ring-entangled…
Network Intrusion DetectionAnomaly DetectionMachine Unlearning for the XGBoost Model with Network Intrusion Datasets
Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image…
Network Intrusion DetectionTimestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection
Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs). However, most existing GNN…
Network Intrusion DetectionComputational EfficiencyRepresentation LearningContrastive LearningCategorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that lea…
Network Intrusion DetectionEvaluation of AutoML Frameworks for IDS under Imbalanced Data Conditions of the NSL-KDD Dataset
This work investigates the impact of severe class imbalance on the performance of automated machine learning (AutoML) frameworks for multiclass network intrusion detection using the NSL-KDD dataset. Unlike previous studi…
Network Intrusion DetectionHyperparameter OptimizationBinary ClassificationEnsemble LearningDo Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017. Howeve…
Network Intrusion DetectionnCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection
Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank feat…
Network Intrusion DetectionOn the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection
Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives …
Network Intrusion DetectionA Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)
Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.3 and QUIC, where payload inspe…
Network Intrusion DetectionMulti-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection
Network Intrusion Detection System is a critical means of ensuring cybersecurity. However, existing Genetic Algorithm-based feature selection methods face several limitations when dealing with high-dimensional redundant …
Network Intrusion DetectionA No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning (ML) models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently rob…
Network Intrusion DetectionFew-Shot Network Intrusion Detection Using Online Triplet Mining
Network intrusion detection systems play a vital role in protecting networks by detecting malicious network traffic which can then be investigated by a cybersecurity operations centre. State-of-the-art approaches utilise…
Network Intrusion DetectionAnomaly DetectionEnhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversa…
Network Intrusion DetectionAdversarial RobustnessAdversarial DefenseEvaluating Tabular Representation Learning for Network Intrusion Detection
Classic Network Intrusion Detection Systems (NIDS) often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to t…
Unsupervised Anomaly DetectionNetwork Intrusion DetectionRepresentation LearningFeature EngineeringRobust and Explainable Divide-and-Conquer Learning for Intrusion Detection
Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such models remains impractical on resource-const…
Network Intrusion DetectionAdversarial Robustness