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 561–570 of 800 papers

TitleStatusHype
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—0
Training a quantum annealing based restricted Boltzmann machine on cybersecurity data—0
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Adversarial Examples in Constrained Domains—0
Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort—0
DualNet: Locate Then Detect Effective Payload with Deep Attention Network—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—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