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 661–670 of 800 papers

TitleStatusHype
Sparse Bayesian approach for metric learning in latent spaceCode0
Omni SCADA Intrusion Detection Using Deep Learning Algorithms—0
Road Context-aware Intrusion Detection System for Autonomous Cars—0
Learning Neural Representations for Network Anomaly DetectionCode0
Data Analysis of Wireless Networks Using Classification Techniques—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle—0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—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