Intrusion Detection
6개 벤치마크 · 논문 987편 · 이 태스크의 논문 보기 →
Benchmarks
CICIDS2017
20NewsGroups
CIC-DDoS
CIC-DoS
UNSW-NB15
^(#$!@#$)(()))******
Most implemented
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
Papers
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 Detection