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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 800 papers

TitleStatusHype
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Adversarial Examples in Constrained Domains—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—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
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—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
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—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
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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