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 241–250 of 800 papers

TitleStatusHype
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
Real-time Network Intrusion Detection via Decision Transformers—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
Intrusion Detection System with Machine Learning and Multiple Datasets—0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—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