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 221230 of 800 papers

TitleStatusHype
Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics0
Adversarial Machine Learning in Network Intrusion Detection Systems0
A Content-Based Deep Intrusion Detection System0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data0
A cognitive based Intrusion detection system0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?0
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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