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

Showing 5160 of 800 papers

TitleStatusHype
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs0
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD0
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection0
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset0
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 Grid0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids0
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
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Benchmark Results

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