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 681–690 of 800 papers

TitleStatusHype
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Adversarial Examples in Constrained Domains—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