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 471–480 of 800 papers

TitleStatusHype
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—0
GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Show:102550
← PrevPage 48 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