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 241–250 of 800 papers

TitleStatusHype
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
Blockchain Large Language Models—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure—0
Deep Learning-based Intrusion Detection Systems: A Survey—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Show:102550
← PrevPage 25 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