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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 800 papers

TitleStatusHype
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
A flow-based IDS using Machine Learning in eBPFCode1
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Simplified and Secure MCP Gateways for Enterprise AI IntegrationCode1
Sketch-Based Anomaly Detection in Streaming GraphsCode1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
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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