eprintid: 17340
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/73/40
datestamp: 2023-12-19 03:23:45
lastmod: 2023-12-19 03:23:45
status_changed: 2023-12-19 03:07:53
type: article
metadata_visibility: show
creators_name: Alashhab, A.A.
creators_name: Zahid, M.S.M.
creators_name: Muneer, A.
creators_name: Abdukkahi, M.
title: Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks
ispublished: pub
keywords: Brain; Cybersecurity; Denial-of-service attack; Internet of things; Network security, Attack detection; Deep learning; Denialof- service attacks; Distributed denial of service; Lddos attack; Low rates; Network-based; Openflow; Sdn; Software-defined networks, Long short-term memory
note: cited By 8
abstract: Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on high-rate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There�s a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
date: 2022
publisher: Science and Information Organization
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143862752&doi=10.14569%2fIJACSA.2022.0131141&partnerID=40&md5=3f208e9a863aa0487425bdc070cf1263
id_number: 10.14569/IJACSA.2022.0131141
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 13
number: 11
pagerange: 371-377
refereed: TRUE
issn: 2158107X
citation:   Alashhab, A.A. and Zahid, M.S.M. and Muneer, A. and Abdukkahi, M.  (2022) Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks.  International Journal of Advanced Computer Science and Applications, 13 (11).  pp. 371-377.  ISSN 2158107X