dc.contributor.author |
Machaka, P
|
|
dc.contributor.author |
Bagula, A
|
|
dc.contributor.author |
Nelwamondo, Fulufhelo V
|
|
dc.date.accessioned |
2017-07-28T08:59:55Z |
|
dc.date.available |
2017-07-28T08:59:55Z |
|
dc.date.issued |
2016-11 |
|
dc.identifier.citation |
Machaka, P., Bagula, A. and Nelwamondo, F.V. 2016. Using exponentially weighted moving average algorithm to defend against DDoS attacks. 2016 International Conference on Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 30 November to 2 December 2016, Stellenbosch, South Africa, Cape Town. 10.1109/RoboMech.2016.7813157 |
en_US |
dc.identifier.isbn |
978-1-5090-3336-2 |
|
dc.identifier.uri |
http://ieeexplore.ieee.org/document/7813157/
|
|
dc.identifier.uri |
10.1109/RoboMech.2016.7813157
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/9316
|
|
dc.description |
2016 International Conference on Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 30 November to 2 December 2016, Stellenbosch, South Africa, Cape Town |
en_US |
dc.description.abstract |
This paper seeks to investigate the performance of the Exponentially Weighted Moving Average (EWMA) for mining big data and detection of DDoS attacks in Internet of Things (IoT) infrastructure. The paper will investigate the tradeoff between the algorithm’s detection rate, false alarm and detection delay. The paper seeks to further investigate how the performance of the algorithm is affected by the tuning parameters and how various network attack intensity affect its performance. The performance results are analyzed and discussed and further suggestion is also discussed. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;18352 |
|
dc.subject |
Change detection |
en_US |
dc.subject |
Distributed denial of service |
en_US |
dc.subject |
TCP-SYN flooding |
en_US |
dc.subject |
Exponentially weighted moving average |
en_US |
dc.subject |
EWMA |
en_US |
dc.subject |
2016 International Conference on Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech) |
en_US |
dc.title |
Using exponentially weighted moving average algorithm to defend against DDoS attacks |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Machaka, P., Bagula, A., & Nelwamondo, F. V. (2016). Using exponentially weighted moving average algorithm to defend against DDoS attacks. IEEE. http://hdl.handle.net/10204/9316 |
en_ZA |
dc.identifier.chicagocitation |
Machaka, P, A Bagula, and Fulufhelo V Nelwamondo. "Using exponentially weighted moving average algorithm to defend against DDoS attacks." (2016): http://hdl.handle.net/10204/9316 |
en_ZA |
dc.identifier.vancouvercitation |
Machaka P, Bagula A, Nelwamondo FV, Using exponentially weighted moving average algorithm to defend against DDoS attacks; IEEE; 2016. http://hdl.handle.net/10204/9316 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Machaka, P
AU - Bagula, A
AU - Nelwamondo, Fulufhelo V
AB - This paper seeks to investigate the performance of the Exponentially Weighted Moving Average (EWMA) for mining big data and detection of DDoS attacks in Internet of Things (IoT) infrastructure. The paper will investigate the tradeoff between the algorithm’s detection rate, false alarm and detection delay. The paper seeks to further investigate how the performance of the algorithm is affected by the tuning parameters and how various network attack intensity affect its performance. The performance results are analyzed and discussed and further suggestion is also discussed.
DA - 2016-11
DB - ResearchSpace
DP - CSIR
KW - Change detection
KW - Distributed denial of service
KW - TCP-SYN flooding
KW - Exponentially weighted moving average
KW - EWMA
KW - 2016 International Conference on Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)
LK - https://researchspace.csir.co.za
PY - 2016
SM - 978-1-5090-3336-2
T1 - Using exponentially weighted moving average algorithm to defend against DDoS attacks
TI - Using exponentially weighted moving average algorithm to defend against DDoS attacks
UR - http://hdl.handle.net/10204/9316
ER -
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en_ZA |