Inter-dataset generalization strength of supervised machine learning methods for intrusion detection
This article describes an experimental investigation into the inter-dataset generalization of
supervised machine learning methods, trained to distinguish between benign and several …
supervised machine learning methods, trained to distinguish between benign and several …
Towards model generalization for intrusion detection: Unsupervised machine learning techniques
Through the ongoing digitization of the world, the number of connected devices is
continuously growing without any foreseen decline in the near future. In particular, these …
continuously growing without any foreseen decline in the near future. In particular, these …
A review on challenges and future research directions for machine learning-based intrusion detection system
Research in the field of Intrusion Detection is focused on developing an efficient strategy that
can identify network attacks. One of the important strategies is to supervise the network …
can identify network attacks. One of the important strategies is to supervise the network …
Advancing Cyber Defense: Machine Learning Techniques for NextGeneration Intrusion Detection
BR Chirra - International Journal of Machine Learning Research in …, 2023 - ijmlrcai.com
The rapid evolution of cyber threats has made traditional intrusion detection systems (IDS)
increasingly ineffective in addressing sophisticated attacks. To combat this challenge, the …
increasingly ineffective in addressing sophisticated attacks. To combat this challenge, the …
Effect of balancing data using synthetic data on the performance of machine learning classifiers for intrusion detection in computer networks
Attacks on computer networks have increased significantly in recent days, due in part to the
availability of sophisticated tools for launching such attacks as well as the thriving …
availability of sophisticated tools for launching such attacks as well as the thriving …
Machine learning-based intrusion detection: feature selection versus feature extraction
Abstract Internet of Things (IoTs) has been playing an important role in many sectors, such
as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT …
as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT …
Ensemble classification for intrusion detection via feature extraction based on deep Learning
An intrusion detection system is a security system that aims to detect sabotage and
intrusions on networks to inform experts of the attack and abuse of the network. Different …
intrusions on networks to inform experts of the attack and abuse of the network. Different …
Intrusion detection based on machine learning techniques in computer networks
AS Dina, D Manivannan - Internet of Things, 2021 - Elsevier
Intrusions in computer networks have increased significantly in the last decade, due in part
to a profitable underground cyber-crime economy and the availability of sophisticated tools …
to a profitable underground cyber-crime economy and the availability of sophisticated tools …
MSML: A novel multilevel semi-supervised machine learning framework for intrusion detection system
Intrusion detection technology has received increasing attention in recent years. Many
researchers have proposed various intrusion detection systems using machine learning …
researchers have proposed various intrusion detection systems using machine learning …
Effects of feature selection and normalization on network intrusion detection
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and
approaches led to using Machine Learning (ML) techniques to build more efficient and …
approaches led to using Machine Learning (ML) techniques to build more efficient and …