Recursive feature elimination with cross-validation with decision tree: Feature selection method for machine learning-based intrusion detection systems
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network
I Sumaiya Thaseen, J Saira Banu… - Transactions on …, 2021 - Wiley Online Library
Serious concerns regarding vulnerability and security have been raised as a result of the
constant growth of computer networks. Intrusion detection systems (IDS) have been adopted …
constant growth of computer networks. Intrusion detection systems (IDS) have been adopted …
Intrusion detection system based on a modified binary grey wolf optimisation
One critical issue within network security refers to intrusion detection. The nature of intrusion
attempts appears to be nonlinear, wherein the network traffic performance is unpredictable …
attempts appears to be nonlinear, wherein the network traffic performance is unpredictable …
Classification model for accuracy and intrusion detection using machine learning approach
In today's cyber world, the demand for the internet is increasing day by day, increasing the
concern of network security. The aim of an Intrusion Detection System (IDS) is to provide …
concern of network security. The aim of an Intrusion Detection System (IDS) is to provide …
Intrusion detection system using hybrid classifiers with meta-heuristic algorithms for the optimization and feature selection by genetic algorithm
An intrusion detection system (IDS) is considered critical for detecting threats, intrusions, and
unauthorized access. IDS monitors massive network traffic that includes irrelevant and …
unauthorized access. IDS monitors massive network traffic that includes irrelevant and …
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods
M Alkasassbeh - arXiv preprint arXiv:1712.09623, 2017 - arxiv.org
Despite the great developments in information technology, particularly the Internet, computer
networks, global information exchange, and its positive impact in all areas of daily life, it has …
networks, global information exchange, and its positive impact in all areas of daily life, it has …
A survey of intrusion detection systems based on ensemble and hybrid classifiers
AA Aburomman, MBI Reaz - Computers & security, 2017 - Elsevier
Due to the frequency of malicious network activities and network policy violations, intrusion
detection systems (IDSs) have emerged as a group of methods that combats the …
detection systems (IDSs) have emerged as a group of methods that combats the …
Attack classification using feature selection techniques: a comparative study
The goal of securing a network is to protect the information flowing through the network and
to ensure the security of intellectual as well as sensitive data for the underlying application …
to ensure the security of intellectual as well as sensitive data for the underlying application …
A hybrid feature selection for network intrusion detection systems: Central points
N Moustafa, J Slay - arXiv preprint arXiv:1707.05505, 2017 - arxiv.org
Network intrusion detection systems are an active area of research to identify threats that
face computer networks. Network packets comprise of high dimensions which require huge …
face computer networks. Network packets comprise of high dimensions which require huge …
Decision tree classifier for network intrusion detection with GA-based feature selection
Machine Learning techniques such as Genetic Algorithms and Decision Trees have been
applied to the field of intrusion detection for more than a decade. Machine Learning …
applied to the field of intrusion detection for more than a decade. Machine Learning …