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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 800 papers

TitleStatusHype
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Open Set Dandelion Network for IoT Intrusion Detection—0
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
netFound: Foundation Model for Network SecurityCode1
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
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
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection—0
Efficient Network Representation for GNN-based Intrusion Detection—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
Multidomain transformer-based deep learning for early detection of network intrusion—0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems—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