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 451–475 of 800 papers

TitleStatusHype
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Orthogonal variance-based feature selection for intrusion detection systems—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
From Zero-Shot Machine Learning to Zero-Day Attack Detection—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
A Novel Online Incremental Learning Intrusion Prevention System—0
Modern Cybersecurity Solution using Supervised Machine Learning—0
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—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