dc.contributor.author |
Umba, SMW
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
|
|
dc.contributor.author |
Ramotsoela, D
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|
dc.date.accessioned |
2023-03-08T12:17:13Z |
|
dc.date.available |
2023-03-08T12:17:13Z |
|
dc.date.issued |
2022-04 |
|
dc.identifier.citation |
Umba, S., Abu-Mahfouz, A.M. & Ramotsoela, D. 2022. Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems. <i>International Journal of Environmental Research and Public Health, 19(9).</i> http://hdl.handle.net/10204/12662 |
en_ZA |
dc.identifier.issn |
1660-4601 |
|
dc.identifier.issn |
1661-7827 |
|
dc.identifier.uri |
https://doi.org/10.3390/ijerph19095367
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/12662
|
|
dc.description.abstract |
Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amount of data, vital and critical information can be exchanged between the different parts of a WSN, good management and protection schemes are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial Intelligence techniques (Decision Tree, Naïve Bayes, and Deep Artificial Neural Network) are trained to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based anomaly detector yields the best performances metrics both in the binary classification and in the multinomial classification. Additionally, it was found that an IDS using the Naïve Bayes-based anomaly detector was only adapted for binary classification of intrusions in low memory capacity SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was found that the Deep Artificial Neural Network should be expected to become the next default anomaly detector in the light of its current performance metrics and the increasing abundance of training data. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/1660-4601/19/9/5367 |
en_US |
dc.source |
International Journal of Environmental Research and Public Health, 19(9) |
en_US |
dc.subject |
Artificial intelligence |
en_US |
dc.subject |
Deep learning |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
Intrusion detection |
en_US |
dc.subject |
Healthcare |
en_US |
dc.subject |
Software-Defined Wireless Sensor Network |
en_US |
dc.subject |
SDWSN |
en_US |
dc.subject |
Wireless sensor network |
en_US |
dc.title |
Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
21pp |
en_US |
dc.description.note |
© 2022 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 |
Umba, S., Abu-Mahfouz, A. M., & Ramotsoela, D. (2022). Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems. <i>International Journal of Environmental Research and Public Health, 19(9)</i>, http://hdl.handle.net/10204/12662 |
en_ZA |
dc.identifier.chicagocitation |
Umba, SMW, Adnan MI Abu-Mahfouz, and D Ramotsoela "Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems." <i>International Journal of Environmental Research and Public Health, 19(9)</i> (2022) http://hdl.handle.net/10204/12662 |
en_ZA |
dc.identifier.vancouvercitation |
Umba S, Abu-Mahfouz AM, Ramotsoela D. Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems. International Journal of Environmental Research and Public Health, 19(9). 2022; http://hdl.handle.net/10204/12662. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Umba, SMW
AU - Abu-Mahfouz, Adnan MI
AU - Ramotsoela, D
AB - Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amount of data, vital and critical information can be exchanged between the different parts of a WSN, good management and protection schemes are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial Intelligence techniques (Decision Tree, Naïve Bayes, and Deep Artificial Neural Network) are trained to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based anomaly detector yields the best performances metrics both in the binary classification and in the multinomial classification. Additionally, it was found that an IDS using the Naïve Bayes-based anomaly detector was only adapted for binary classification of intrusions in low memory capacity SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was found that the Deep Artificial Neural Network should be expected to become the next default anomaly detector in the light of its current performance metrics and the increasing abundance of training data.
DA - 2022-04
DB - ResearchSpace
DP - CSIR
J1 - International Journal of Environmental Research and Public Health, 19(9)
KW - Artificial intelligence
KW - Deep learning
KW - Internet of Things
KW - Intrusion detection
KW - Healthcare
KW - Software-Defined Wireless Sensor Network
KW - SDWSN
KW - Wireless sensor network
LK - https://researchspace.csir.co.za
PY - 2022
SM - 1660-4601
SM - 1661-7827
T1 - Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems
TI - Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems
UR - http://hdl.handle.net/10204/12662
ER -
|
en_ZA |
dc.identifier.worklist |
26241 |
en_US |