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 381–390 of 800 papers

TitleStatusHype
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities—0
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
CoAP-DoS: An IoT Network Intrusion Dataset—0
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Show:102550
← PrevPage 39 of 80Next →
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