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 281–290 of 800 papers

TitleStatusHype
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—0
DualNet: Locate Then Detect Effective Payload with Deep Attention Network—0
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection—0
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware—0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges—0
ECU Identification using Neural Network Classification and Hyperparameter Tuning—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—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