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 451–475 of 800 papers

TitleStatusHype
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
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids—0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars—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