Robust genetic machine learning ensemble model for intrusion detection in network traffic
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Robust genetic machine learning ensemble model for intrusion detection in network traffic ; volume:13 ; number:1 ; day:11 ; month:10 ; year:2023 ; pages:1-22 ; date:12.2023
Scientific reports ; 13, Heft 1 (11.10.2023), 1-22, 12.2023
- Urheber
-
Akhtar, Muhammad Ali
Qadri, Syed Muhammad Owais
Siddiqui, Maria Andleeb
Mustafa, Syed Muhammad Nabeel
Javaid, Saba
Ali, Syed Abbas
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-023-43816-1
- URN
-
urn:nbn:de:101:1-2024020515070693145692
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:35 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Akhtar, Muhammad Ali
- Qadri, Syed Muhammad Owais
- Siddiqui, Maria Andleeb
- Mustafa, Syed Muhammad Nabeel
- Javaid, Saba
- Ali, Syed Abbas
- SpringerLink (Online service)
Ähnliche Objekte (12)
Machine learning for application layer intrusion detection
Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection
Machine Learning Models in Network Intrusion Detection Systems : Self-Supervised Detection of Malicious Flows and Traffic Patterns Recognition in Programmable Networks
Enhancing intrusion detection: a hybrid machine and deep learning approach
Intrusion detection model using machine learning algorithm on Big Data environment
MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs
Self-healing hybrid intrusion detection system: an ensemble machine learning approach
Towards adversarial realism and robust learning for IoT intrusion detection and classification
Robust machine vision for service robotics
Robust machine vision for service robotics
Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset
Maximizing intrusion detection efficiency for IoT networks using extreme learning machine
Machine learning for application layer intrusion detection
Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection
Machine Learning Models in Network Intrusion Detection Systems : Self-Supervised Detection of Malicious Flows and Traffic Patterns Recognition in Programmable Networks
Enhancing intrusion detection: a hybrid machine and deep learning approach
Intrusion detection model using machine learning algorithm on Big Data environment
MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs
Self-healing hybrid intrusion detection system: an ensemble machine learning approach
Towards adversarial realism and robust learning for IoT intrusion detection and classification
Robust machine vision for service robotics
Robust machine vision for service robotics
Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset
Maximizing intrusion detection efficiency for IoT networks using extreme learning machine
Machine learning for application layer intrusion detection
Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection
Machine Learning Models in Network Intrusion Detection Systems : Self-Supervised Detection of Malicious Flows and Traffic Patterns Recognition in Programmable Networks
Enhancing intrusion detection: a hybrid machine and deep learning approach
Intrusion detection model using machine learning algorithm on Big Data environment
MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs
Self-healing hybrid intrusion detection system: an ensemble machine learning approach
Towards adversarial realism and robust learning for IoT intrusion detection and classification
Robust machine vision for service robotics
Robust machine vision for service robotics
Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset