Evaluation of classification algorithms for intrusion detection system: A review
Intrusion detection is one of the most critical network security problems in the technology
world. Machine learning techniques are being implemented to improve the Intrusion …
world. Machine learning techniques are being implemented to improve the Intrusion …
Machine learning techniques for network anomaly detection: A survey
Nowadays, distributed data processing in cloud computing has gained increasing attention
from many researchers. The intense transfer of data has made the network an attractive and …
from many researchers. The intense transfer of data has made the network an attractive and …
Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection …
To protect the network, resources, and sensitive data, the intrusion detection system (IDS)
has become a fundamental component of organizations that prevents cybercriminal …
has become a fundamental component of organizations that prevents cybercriminal …
[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
Hybrid machine learning for network anomaly intrusion detection
In this paper, a hybrid approach of combing two machine learning algorithms is proposed to
detect the different possible attacks by performing effective feature selection and …
detect the different possible attacks by performing effective feature selection and …
Machine learning for misuse-based network intrusion detection: overview, unified evaluation and feature choice comparison framework
Network Intrusion detection systems are essential for the protection of advanced
communication networks. Originally, these systems were hard-coded to identify specific …
communication networks. Originally, these systems were hard-coded to identify specific …
Machine learning techniques for anomaly-based detection system on CSE-CIC-IDS2018 dataset
Anomaly-based detection is a novel form of an intrusion detection system, which has
become the focus of many researchers for cybersecurity systems. Data manages most …
become the focus of many researchers for cybersecurity systems. Data manages most …
Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering
Network security is crucial in today's digital world, since there are multiple ongoing threats to
sensitive data and vital infrastructure. The aim of this study to improve network security by …
sensitive data and vital infrastructure. The aim of this study to improve network security by …
Lightweight internet of things botnet detection using one-class classification
Like smart phones, the recent years have seen an increased usage of internet of things (IoT)
technology. IoT devices, being resource constrained due to smaller size, are vulnerable to …
technology. IoT devices, being resource constrained due to smaller size, are vulnerable to …
[HTML][HTML] Enhancing intrusion detection systems through dimensionality reduction: A comparative study of machine learning techniques for cyber security
Our research aims to improve automated intrusion detection by develo** a highly accurate
classifier with minimal false alarms. The motivation behind our work is to tackle the …
classifier with minimal false alarms. The motivation behind our work is to tackle the …