dc.contributor.author | Kumar, Sanjay | |
dc.contributor.author | Viinikainen, Ari | |
dc.contributor.author | Hämäläinen, Timo | |
dc.date.accessioned | 2018-11-23T09:32:35Z | |
dc.date.available | 2018-11-23T09:32:35Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Kumar, S., Viinikainen, A., & Hämäläinen, T. (2018). A Network-Based Framework for Mobile Threat Detection. In <i>ICDIS 2018 : 1st International Conference on Data Intelligence and Security</i> (pp. 227-233). IEEE. <a href="https://doi.org/10.1109/ICDIS.2018.00044" target="_blank">https://doi.org/10.1109/ICDIS.2018.00044</a> | |
dc.identifier.other | CONVID_28024181 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/60310 | |
dc.description.abstract | Mobile malware attacks increased three folds in the
past few years and continued to expand with the growing number
of mobile users. Adversary uses a variety of evasion techniques
to avoid detection by traditional systems, which increase the
diversity of malicious applications. Thus, there is a need for an
intelligent system that copes with this issue. This paper proposes
a machine learning (ML) based framework to counter rapid
evolution of mobile threats. This model is based on flow-based
features, that will work on the network side. This model is
designed with adversarial input in mind. The model uses 40 timebased
network flow features, extracted from the real-time traffic
of malicious and benign applications. The proposed model not
only to detects the known and unknown mobile threats but also
deals with the changing behavior of the attackers by triggering
the retraining phase. The proposed framework can be used by
the mobile operators to protect their subscribers. We used several
supervised ML algorithms to build the model and got an average
accuracy of up to 99.8%. | fi |
dc.format.extent | 297 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | ICDIS 2018 : 1st International Conference on Data Intelligence and Security | |
dc.rights | In Copyright | |
dc.subject.other | intrusion detection | |
dc.subject.other | mobile threats | |
dc.subject.other | concept-drift | |
dc.subject.other | anomaly detection | |
dc.title | A Network-Based Framework for Mobile Threat Detection | |
dc.type | conference paper | |
dc.identifier.urn | URN:NBN:fi:jyu-201811154727 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2018-11-15T10:15:07Z | |
dc.relation.isbn | 978-1-5386-5762-1 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 227-233 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © IEEE, 2018. | |
dc.rights.accesslevel | openAccess | fi |
dc.type.publication | conferenceObject | |
dc.relation.conference | International Conference on Data Intelligence and Security | |
dc.subject.yso | tietoturva | |
dc.subject.yso | haittaohjelmat | |
dc.subject.yso | mobiililaitteet | |
dc.subject.yso | koneoppiminen | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5479 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p2837 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4834 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1109/ICDIS.2018.00044 | |
dc.type.okm | A4 | |