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 326–350 of 800 papers

TitleStatusHype
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Ensemble learning techniques for intrusion detection system in the context of cybersecurity—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Evaluation of Machine Learning Algorithms for Intrusion Detection System—0
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
Explainable Intrusion Detection Systems Using Competitive Learning Techniques—0
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—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