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 501–510 of 800 papers

TitleStatusHype
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
GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—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
Jasmine: A New Active Learning Approach to Combat Cybercrime—0
Intrusion Detection In Computer Networks Using Machine Learning AlgorithmsCode0
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks—0
Show:102550
← PrevPage 51 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