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 26–50 of 800 papers

TitleStatusHype
Kairos: Practical Intrusion Detection and Investigation using Whole-system ProvenanceCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial NetworksCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-setsCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Unveiling the potential of Graph Neural Networks for robust Intrusion DetectionCode1
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
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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