A review of recent approaches on wrapper feature selection for intrusion detection
In this paper, we present a review of recent advances in wrapper feature selection
techniques for attack detection and classification, applied in intrusion detection area. Due to …
techniques for attack detection and classification, applied in intrusion detection area. Due to …
[PDF][PDF] Predicting DOS-DDOS attacks: Review and evaluation study of feature selection methods based on wrapper process
K Bouzoubaa, Y Taher, B Nsiri - Int. J. Adv. Comput. Sci. Appl, 2021 - academia.edu
Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting
a growing threat to individuals, private and public sectors, especially the Denial Of Service …
a growing threat to individuals, private and public sectors, especially the Denial Of Service …
Feature selection and ensemble-based intrusion detection system: an efficient and comprehensive approach
E Jaw, X Wang - Symmetry, 2021 - mdpi.com
The emergence of ground-breaking technologies such as artificial intelligence, cloud
computing, big data powered by the Internet, and its highly valued real-world applications …
computing, big data powered by the Internet, and its highly valued real-world applications …
Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications
M Rani, Gagandeep - Multimedia Tools and Applications, 2022 - Springer
Abstract The Intrusion Detection System plays a significant role in discovering malicious
activities and provides better network security solutions than other conventional defense …
activities and provides better network security solutions than other conventional defense …
Numerical feature selection and hyperbolic tangent feature scaling in machine learning-based detection of anomalies in the computer network behavior
Anomaly-based intrusion detection systems identify the computer network behavior which
deviates from the statistical model of typical network behavior. Binary classifiers based on …
deviates from the statistical model of typical network behavior. Binary classifiers based on …
Network Intrusion Detection: An IoT and Non IoT-Related Survey
The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing.
The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent …
The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent …
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 …
DDoS attacks detection based on machine learning algorithms in IoT environments
In today's digital era, most electrical gadgets have become smart, and the great majority of
them can connect to the internet. The Internet of Things (IoT) refers to a network comprised …
them can connect to the internet. The Internet of Things (IoT) refers to a network comprised …
Hybrid feature selection method for intrusion detection systems based on an improved intelligent water drop algorithm
A critical task and a competitive research area is to secure networks against attacks. One of
the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning …
the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning …
Feature selection algorithm characterization for NIDS using machine and deep learning
J Verma, A Bhandari, G Singh - 2022 IEEE International IOT …, 2022 - ieeexplore.ieee.org
Data dimensionality is increasing at a rapid rate, posing difficulties for traditional mining and
learning algorithms. Commercial NIDS models make use of statistical measures to analyze …
learning algorithms. Commercial NIDS models make use of statistical measures to analyze …