SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 51–100 of 800 papers

TitleStatusHype
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems—0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs—0
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD—0
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection—0
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset—0
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids—0
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless NetworksCode1
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset—0
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions—0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Mapping the Landscape of Generative AI in Network Monitoring and Management—0
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries—0
Technical Report: Generating the WEB-IDS23 Dataset—0
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing—0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
Flow Exporter Impact on Intelligent Intrusion Detection Systems—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model SelectionCode0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified