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 551–560 of 800 papers

TitleStatusHype
Explaining Network Intrusion Detection System Using Explainable AI Framework—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
TINKER: A framework for Open source Cyberthreat Intelligence—0
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data—0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware—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