dc.contributor.author |
Adedeji, KB
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
|
|
dc.contributor.author |
Kurien, AM
|
|
dc.date.accessioned |
2024-02-05T09:22:23Z |
|
dc.date.available |
2024-02-05T09:22:23Z |
|
dc.date.issued |
2023-07 |
|
dc.identifier.citation |
Adedeji, K., Abu-Mahfouz, A.M. & Kurien, A. 2023. DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges. <i>Journal of Sensor and Actuator Networks, 12(4).</i> http://hdl.handle.net/10204/13563 |
en_ZA |
dc.identifier.issn |
2224-2708 |
|
dc.identifier.uri |
https://doi.org/10.3390/jsan12040051
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/13563
|
|
dc.description.abstract |
In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek to deplete the resources of the target network by flooding it with numerous spoofed requests from a distributed system. Research studies have demonstrated that a DDoS attack has a considerable impact on the target network resources and can result in an extended operational outage if not detected. The detection of DDoS attacks has been approached using a variety of methods. In this paper, a comprehensive survey of the methods used for DDoS attack detection on selected internet-enabled networks is presented. This survey aimed to provide a concise introductory reference for early researchers in the development and application of attack detection methodologies in IoT-based applications. Unlike other studies, a wide variety of methods, ranging from the traditional methods to machine and deep learning methods, were covered. These methods were classified based on their nature of operation, investigated as to their strengths and weaknesses, and then examined via several research studies which made use of each approach. In addition, attack scenarios and detection studies in emerging networks such as the internet of drones, routing protocol based IoT, and named data networking were also covered. Furthermore, technical challenges in each research study were identified. Finally, some remarks for enhancing the research studies were provided, and potential directions for future research were highlighted. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/2224-2708/12/4/51 |
en_US |
dc.source |
Journal of Sensor and Actuator Networks, 12(4) |
en_US |
dc.subject |
Attack detection |
en_US |
dc.subject |
Cyber security |
en_US |
dc.subject |
DDoS attack |
en_US |
dc.subject |
Deep learning |
en_US |
dc.subject |
Entropy |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Machine learning |
en_US |
dc.title |
DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
57 |
en_US |
dc.description.note |
©2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Adedeji, K., Abu-Mahfouz, A. M., & Kurien, A. (2023). DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges. <i>Journal of Sensor and Actuator Networks, 12(4)</i>, http://hdl.handle.net/10204/13563 |
en_ZA |
dc.identifier.chicagocitation |
Adedeji, KB, Adnan MI Abu-Mahfouz, and AM Kurien "DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges." <i>Journal of Sensor and Actuator Networks, 12(4)</i> (2023) http://hdl.handle.net/10204/13563 |
en_ZA |
dc.identifier.vancouvercitation |
Adedeji K, Abu-Mahfouz AM, Kurien A. DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges. Journal of Sensor and Actuator Networks, 12(4). 2023; http://hdl.handle.net/10204/13563. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Adedeji, KB
AU - Abu-Mahfouz, Adnan MI
AU - Kurien, AM
AB - In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek to deplete the resources of the target network by flooding it with numerous spoofed requests from a distributed system. Research studies have demonstrated that a DDoS attack has a considerable impact on the target network resources and can result in an extended operational outage if not detected. The detection of DDoS attacks has been approached using a variety of methods. In this paper, a comprehensive survey of the methods used for DDoS attack detection on selected internet-enabled networks is presented. This survey aimed to provide a concise introductory reference for early researchers in the development and application of attack detection methodologies in IoT-based applications. Unlike other studies, a wide variety of methods, ranging from the traditional methods to machine and deep learning methods, were covered. These methods were classified based on their nature of operation, investigated as to their strengths and weaknesses, and then examined via several research studies which made use of each approach. In addition, attack scenarios and detection studies in emerging networks such as the internet of drones, routing protocol based IoT, and named data networking were also covered. Furthermore, technical challenges in each research study were identified. Finally, some remarks for enhancing the research studies were provided, and potential directions for future research were highlighted.
DA - 2023-07
DB - ResearchSpace
DP - CSIR
J1 - Journal of Sensor and Actuator Networks, 12(4)
KW - Attack detection
KW - Cyber security
KW - DDoS attack
KW - Deep learning
KW - Entropy
KW - Internet of Things
KW - IoT
KW - Machine learning
LK - https://researchspace.csir.co.za
PY - 2023
SM - 2224-2708
T1 - DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges
TI - DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges
UR - http://hdl.handle.net/10204/13563
ER -
|
en_ZA |
dc.identifier.worklist |
27497 |
en_US |