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 426–450 of 800 papers

TitleStatusHype
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review—0
Survey of Graph Neural Network for Internet of Things and NextG Networks—0
Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things—0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process—0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—0
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks—0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic—0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Technical Report: Generating the WEB-IDS23 Dataset—0
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems—0
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction—0
The Dendritic Cell Algorithm for Intrusion Detection—0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
The importance of the clustering model to detect new types of intrusion in data traffic—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Time-Based CAN Intrusion Detection Benchmark—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—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