Papers Intrusion Detection
“Intrusion Detection” 태그가 달린 논문 987편 · 필터 해제
Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks
Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent, and supports an adaptive response under severe class imbalance and n…
Intrusion DetectionExplainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments
Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time. We present an intrusion detection system that addresse…
Intrusion DetectionFeature ImportanceImproving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation
This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data. The approach combines adaptive weighting with incremental f…
Intrusion DetectionAnomaly DetectionOn the Impact of Entropy-based Features
Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features. In this work, we explore the…
Intrusion DetectionFeature EngineeringAnomaly DetectionF-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks
The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learnin…
Intrusion DetectionRES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection
Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift, sensor noise, changing workloads, and evolving attack behaviour. Unde…
Intrusion DetectionCross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment. Most reported resu…
Adversarial RobustnessDomain GeneralizationIntrusion DetectionForensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat …
Synthetic Data GenerationBinary ClassificationIntrusion DetectionGenerative AI and Federated Learning for Intrusion Detection Systems: A Survey
Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. H…
Intrusion DetectionFederated LearningData AugmentationAnomaly DetectionComparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks
The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. G…
Intrusion DetectionCAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation
The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge. Intrusion Detection Systems (IDS) have been widely studied as a defense mechanism for detecting mal…
Intrusion DetectionPLAA: Packet-level Adversarial Attacks in Network Traffic Detection
Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly susceptible to adversarial attacks, which generate malicious traffic …
Intrusion DetectionAdversarial AttackA Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids
The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort…
Intrusion DetectionFrom CVE to CWE: Syscall-Based HIDS Generalisation
Host intrusion detection systems (HIDS) based on system-call traces are typically trained and evaluated against individual Common Vulnerabilities and Exposures (CVE) instances. In operational settings, however, defenders…
Intrusion DetectionSynthetic Network Packet Generation through Statistical Learning and Genetic Algorithms
Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions across both benign and malicious activity. Existing public datasets suff…
Adversarial RobustnessIntrusion DetectionMIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba
The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security thr…
Intrusion DetectionGraph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict
Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management in a ph…
Intrusion DetectionAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and …
Supervised Anomaly DetectionIntrusion DetectionSHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems
Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers in…
Intrusion DetectionCognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduc…
Intrusion DetectionFederated Learning