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 101–150 of 800 papers

TitleStatusHype
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Anomaly Detection in Intra-Vehicle Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Anomaly Detection via Federated Learning—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A Content-Based Deep Intrusion Detection System—0
A cognitive based Intrusion detection system—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Adversarial Examples in Constrained Domains—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
A concise method for feature selection via normalized frequencies—0
Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection -- An Analysis on CIC-AWS-2018 dataset—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
An AutoML-based approach for Network Intrusion Detection—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—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