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
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset—0
Hybrid Model For Intrusion Detection Systems—0
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems—0
Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System—0
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation—0
IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset—0
Image Classifiers for Network Intrusions—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