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 126–150 of 800 papers

TitleStatusHype
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