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 541550 of 800 papers

TitleStatusHype
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier0
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset0
Multi-Source Data Fusion for Cyberattack Detection in Power Systems0
Time-Based CAN Intrusion Detection Benchmark0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks0
Towards Network Traffic Monitoring Using Deep Transfer Learning0
Show:102550
← PrevPage 55 of 80Next →

Benchmark Results

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