dc.contributor.author |
Masengo Wa Umba, S
|
|
dc.contributor.author |
Ramotsoela, TD
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
|
|
dc.contributor.author |
Hancke, GP
|
|
dc.date.accessioned |
2019-10-14T08:03:54Z |
|
dc.date.available |
2019-10-14T08:03:54Z |
|
dc.date.issued |
2019-06 |
|
dc.identifier.citation |
Masengo Wa Umba, S., Ramotsoela, T.D., Abu-Mahfouz, A.M.I. and Hancke, G.P. 2019. Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks. IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, Canada, 12-14 June 2019 pp 2220-2225 |
en_US |
dc.identifier.isbn |
978-1-7281-3666-0 |
|
dc.identifier.isbn |
978-1-7281-3667-7 |
|
dc.identifier.uri |
https://ieeexplore.ieee.org/document/8781114
|
|
dc.identifier.uri |
DOI: 10.1109/ISIE.2019.8781114
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/11167
|
|
dc.description |
Copyright: 2019 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. |
en_US |
dc.description.abstract |
Nowadays, Wireless Sensor Networks (WSNs) are intensively used in highly sensitive environments such as water treatment plants, airports and hospitals. For this reason, the security of communications in WSNs is a very critical problem that must be tackled accordingly. A Software-defined network (SDN) is an architecture aimed at making networks more agile and flexible. A Software-Defined Wireless Sensor Network (SDWSN) is realized by infusing a Software Defined Network (SDN) model in a WSN. In this paper, three Artificial Intelligence (AI) approaches (decision tree, naïve Bayes and deep artificial neural network) used as intrusion detection systems (IDSs) in SDWSNs are analyzed and the results show that the decision tree approach is the best approach for implementing IDSs in classical SDWSNs given its performances. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;22662 |
|
dc.subject |
Artificial Intelligence |
en_US |
dc.subject |
AI |
en_US |
dc.subject |
Software-Defined Wireless Sensor Network |
en_US |
dc.subject |
SDWSN |
en_US |
dc.subject |
Wireless Sensor Networks |
en_US |
dc.subject |
WSN |
en_US |
dc.title |
Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Masengo Wa Umba, S., Ramotsoela, T., Abu-Mahfouz, A. M., & Hancke, G. (2019). Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks. IEEE. http://hdl.handle.net/10204/11167 |
en_ZA |
dc.identifier.chicagocitation |
Masengo Wa Umba, S, TD Ramotsoela, Adnan MI Abu-Mahfouz, and GP Hancke. "Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks." (2019): http://hdl.handle.net/10204/11167 |
en_ZA |
dc.identifier.vancouvercitation |
Masengo Wa Umba S, Ramotsoela T, Abu-Mahfouz AM, Hancke G, Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks; IEEE; 2019. http://hdl.handle.net/10204/11167 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Masengo Wa Umba, S
AU - Ramotsoela, TD
AU - Abu-Mahfouz, Adnan MI
AU - Hancke, GP
AB - Nowadays, Wireless Sensor Networks (WSNs) are intensively used in highly sensitive environments such as water treatment plants, airports and hospitals. For this reason, the security of communications in WSNs is a very critical problem that must be tackled accordingly. A Software-defined network (SDN) is an architecture aimed at making networks more agile and flexible. A Software-Defined Wireless Sensor Network (SDWSN) is realized by infusing a Software Defined Network (SDN) model in a WSN. In this paper, three Artificial Intelligence (AI) approaches (decision tree, naïve Bayes and deep artificial neural network) used as intrusion detection systems (IDSs) in SDWSNs are analyzed and the results show that the decision tree approach is the best approach for implementing IDSs in classical SDWSNs given its performances.
DA - 2019-06
DB - ResearchSpace
DP - CSIR
KW - Artificial Intelligence
KW - AI
KW - Software-Defined Wireless Sensor Network
KW - SDWSN
KW - Wireless Sensor Networks
KW - WSN
LK - https://researchspace.csir.co.za
PY - 2019
SM - 978-1-7281-3666-0
SM - 978-1-7281-3667-7
T1 - Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks
TI - Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks
UR - http://hdl.handle.net/10204/11167
ER -
|
en_ZA |