Network Anomaly Detection Using Probabilistic Data Structures
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Updated
Dec 4, 2021 - C++
Network Anomaly Detection Using Probabilistic Data Structures
Detedcting attacks in intrusion detedtion using RNN
Quick Layered Correlation-based Feature Filtering
Análise de algoritmos de aprendizagem de máquina para identificação de ataques utilizando a base NSL-KDD
Multi-class attack detection on NSL-KDD dataset using TabTransformer
Comparative Analysis of Deep Learning and Machine Learning Models for Network Intrusion Detection
Anomaly IDS using a one-class autoencoder.
Codes for the paper entitled "Optimization of Predictive Performance of Intrusion Detection System Using Hybrid Ensemble Model for Secure Systems"
Research Paper on Ensemble Learning on NSL-KDD Dataset
Intrustion Detection Models based on Internet Traffic Data obtained from the NSL-KDD Dataset
Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System.
A Random Forest model that detects network intrusion and anomalies, using the NSL-KDD dataset.
An in-depth comparison of two prominent intrusion detection datasets: KDDCup99 and NSL-KDD
The following program is capable of analyzing network traffic with multiple different machine learning (ML) and feature selection algorithms to determine whether or not it is malicious.
this repository for Ant Colony Optimization algorithm works
Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System. The deployed project link is as follows.
Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning, Recurrent Neural Network models, MERN web I/O System.
Cyber-attack classification in the network traffic database using NSL-KDD dataset
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