dc.date.accessioned | 2021-10-07T15:11:28Z | |
dc.date.available | 2021-10-07T15:11:28Z | |
dc.date.created | 2021-08-11T12:54:14Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Shahraki, Amin Taherkordi, Amirhosein Haugen, Øystein . TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks. Computer Networks. 2021, 194, 1-13 | |
dc.identifier.uri | http://hdl.handle.net/10852/88814 | |
dc.description.abstract | Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicating without human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc and quasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructures that are able to perform heavyweight network management tasks using, e.g. machine learning-based Network Traffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do not need to be (re-) trained has received much attention due to the time complexity of the training phase. In this study, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), is proposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statistical light-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trends and recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA is then compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detects approximately 60% less false positive alarms than RuLSIF. | |
dc.language | EN | |
dc.publisher | North-Holland | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks | |
dc.type | Journal article | |
dc.creator.author | Shahraki, Amin | |
dc.creator.author | Taherkordi, Amirhosein | |
dc.creator.author | Haugen, Øystein | |
cristin.unitcode | 185,15,5,0 | |
cristin.unitname | Institutt for informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 1925308 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computer Networks&rft.volume=194&rft.spage=1&rft.date=2021 | |
dc.identifier.jtitle | Computer Networks | |
dc.identifier.volume | 194 | |
dc.identifier.doi | https://doi.org/10.1016/j.comnet.2021.108125 | |
dc.identifier.urn | URN:NBN:no-91431 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 1389-1286 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/88814/1/ShahrakiTONTA2021.pdf | |
dc.type.version | PublishedVersion | |
cristin.articleid | 108125 | |