I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Intrusion Detection Schemes Based on Synthetic Minority Ove..:
, In:
2024 IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC)
,
Ali, Ali Hussein
;
Charfeddine, Maha
;
Ammar, Boudour
. - p. 1-8 , 2024
Link:
https://doi.org/10.1109/ISORC61049.2024.10551335
RT T1 <cTitle>2024 IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC)</cTitle>: T1 <Title>Intrusion Detection Schemes Based on Synthetic Minority Oversampling Technique and Machine Learning Models</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10551335&Exemplar=1&LAN=DE A1 Ali, Ali Hussein A1 Charfeddine, Maha A1 Ammar, Boudour A1 Hamed, Bassem Ben YR 2024 SN 2770-162X K1 Radio frequency K1 Accuracy K1 Machine learning algorithms K1 Computer hacking K1 Statistical analysis K1 Intrusion detection K1 Telecommunication traffic K1 Intrusion Detection Systems K1 Machine Learning Techniques K1 Synthetic Minority Oversampling Technique K1 and Feature Selection Approaches SP 1 OP 8 LK http://dx.doi.org/https://doi.org/10.1109/ISORC61049.2024.10551335 DO https://doi.org/10.1109/ISORC61049.2024.10551335 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
