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 601–625 of 800 papers

TitleStatusHype
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
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Towards a graph-based foundation model for network traffic analysis—0
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks—0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Towards Explainable Network Intrusion Detection using Large Language Models—0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
Toward Supervised Anomaly Detection—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Training a quantum annealing based restricted Boltzmann machine on cybersecurity data—0
Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations—0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence—0
Trustworthy Anomaly Detection: A Survey—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—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