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 231240 of 800 papers

TitleStatusHype
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security0
A Cutting-Edge Deep Learning Method For Enhancing IoT Security0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks0
Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
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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