TY  - CONF
AV  - none
SP  - 95
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2023///
TI  - Real-Time Detection of Low-Rate DDoS Attacks in SDN-Based Networks Using Online Machine Learning Model
KW  - Deep learning; E-learning; Intrusion detection; Learning algorithms; Learning systems; Network security; Signal detection
KW  -  Denialof- service attacks; Distributed denial of service; LDDoS attack; Low rates; Machine-learning; Online machine learning; Online machines; Openflow; PA classifier; Software-defined networks
KW  -  Denial-of-service attack
N2  - Software Defined Networks (SDN) provide rapid configuration, scalability, and management through a dynamic, programmable architecture that surpasses traditional network limitations. However, detecting Distributed Denial of Service (DDoS) attacks remains challenging, threatening both traditional and SDN-based networks. Machine Learning (ML) and Deep Learning (DL) technologies in conjunction with SDN have shown significant potential in effectively countering these threats. Prior studies primarily addressed high-rate DDoS attacks, neglecting low-rate DDoS attacks that resemble legitimate traffic, and often using outdated datasets. While researchers employ various offline learning algorithms to identify DDoS attacks, online learning classifiers remain underexplored. Our goal is to offer an intrusion detection model tailored to SDN networks, using the online passive-aggressive classifier. The proposed model achieves a 99.7 average detection rate for normal vs. DDoS network traffic, outperforming similar models on multiple datasets, including (CICDDoS2019, and InSDN. slow-read-DDoS), effectively detecting and mitigating DDoS attacks. © 2023 IEEE.
SN  - 9798350342871
EP  - 101
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182262616&doi=10.1109%2fCSNet59123.2023.10339791&partnerID=40&md5=db14508ffa4621e1c24e908e51a8d273
N1  - cited By 2; Conference of 7th Cyber Security in Networking Conference, CSNet 2023 ; Conference Date: 16 October 2023 Through 18 October 2023; Conference Code:195330
A1  - Alashhab, A.A.
A1  - Zahid, M.S.M.
A1  - Abdullahi, M.
A1  - Rahman, M.S.
ID  - scholars19001
ER  -