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 181–190 of 800 papers

TitleStatusHype
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
Survey of Graph Neural Network for Internet of Things and NextG Networks—0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation—0
Strategic Deployment of Honeypots in Blockchain-based IoT Systems—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—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