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

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Showing 761–770 of 800 papers

TitleStatusHype
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry KeysCode0
On the (Statistical) Detection of Adversarial Examples—0
Learning detectors of malicious web requests for intrusion detection in network traffic—0
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey—0
SoK: Applying Machine Learning in Security - A Survey—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Attribute Learning for Network Intrusion Detection—0
Sorting out typicality with the inverse moment matrix SOS polynomial—0
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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