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

Showing 321330 of 800 papers

TitleStatusHype
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems0
EdgeServe: A Streaming System for Decentralized Model Serving0
CADeSH: Collaborative Anomaly Detection for Smart Homes0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection0
Anomaly based network intrusion detection for IoT attacks using deep learning technique0
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
IoT Botnet Detection Using an Economic Deep Learning Model0
Behavioural Reports of Multi-Stage MalwareCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
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1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified