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 311–320 of 800 papers

TitleStatusHype
Open Set Dandelion Network for IoT Intrusion Detection—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters—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