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
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System—0
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Sdn Intrusion Detection Using Machine Learning Method—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Secure Mobile Crowdsensing with Deep Learning—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection—0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system—0
Security Evaluation of Pattern Classifiers under Attack—0
Show:102550
← PrevPage 56 of 80Next →
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