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 331–340 of 800 papers

TitleStatusHype
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Leveraging Planning Landmarks for Hybrid Online Goal Recognition—0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection—0
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods—0
Ensemble learning techniques for intrusion detection system in the context of cybersecurity—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial NetworksCode0
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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