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 291–300 of 800 papers

TitleStatusHype
EdgeServe: A Streaming System for Decentralized Model Serving—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS—0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks—0
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—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