Preprocessing impact analysis for machine learning-based network intrusion detection
H Güney - Sakarya University Journal of Computer and …, 2023 - saucis.sakarya.edu.tr
Machine learning (ML) has been frequently used to build intelligent systems in many
problem domains, including cybersecurity. For malicious network activity detection, ML …
problem domains, including cybersecurity. For malicious network activity detection, ML …
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 …
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 …
A comparative study of machine learning classifiers for network intrusion detection
The network intrusion detection system (NIDS) has become an essential tool for detecting
attacks in computer networks and protecting the critical information and systems. The …
attacks in computer networks and protecting the critical information and systems. The …
A computationally efficient dimensionality reduction and attack classification approach for network intrusion detection
An intrusion detection system (IDS) is a system that monitors network traffic for malicious
activity and generates alerts. In anomaly-based detection, machine learning (ML) algorithms …
activity and generates alerts. In anomaly-based detection, machine learning (ML) algorithms …
[HTML][HTML] Ids-ml: An open source code for intrusion detection system development using machine learning
Due to the expansion and development of modern networks, the volume and
destructiveness of cyber attacks are continuously increasing. Intrusion Detection Systems …
destructiveness of cyber attacks are continuously increasing. Intrusion Detection Systems …
A Comprehensive Empirical Analysis of Datasets, Regression-Based Feature Selectors and Linear SVM Classifiers for Intrusion Detection Systems
J Azimjonov, T Kim - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Machine learning (ML)-based intrusion detection systems (IDSs) are crucial in safeguarding
computer networks against malicious activities. However, building an optimal (accurate and …
computer networks against malicious activities. However, building an optimal (accurate and …
Intrusion detection using machine learning techniques: an experimental comparison
Due to an exponential increase in the number of cyber-attacks, the need for improved
Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning …
Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning …
Evaluation of machine learning algorithms in network-based intrusion detection system
TH Chua, I Salam - arXiv preprint arXiv:2203.05232, 2022 - arxiv.org
Cybersecurity has become one of the focuses of organisations. The number of cyberattacks
keeps increasing as Internet usage continues to grow. An intrusion detection system (IDS) is …
keeps increasing as Internet usage continues to grow. An intrusion detection system (IDS) is …
A supervised machine learning-based solution for efficient network intrusion detection using ensemble learning based on hyperparameter optimization
An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …