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 201–210 of 800 papers

TitleStatusHype
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Fair Anomaly Detection For Imbalanced Groups—0
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity—0
Towards a graph-based foundation model for network traffic analysis—0
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS—0
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data AnalysisCode0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)—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