| fullrecord |
[{"key": "dc.contributor.advisor", "value": "Hakala, Ismo", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Honkanen, Risto", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Lepp\u00e4nen, Rony", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-12-13T12:56:55Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-12-13T12:56:55Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2019", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/66794", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Globaalista IP-verkkoliikenteest\u00e4 yh\u00e4 suuremmasta osuudesta vastuussa olevat uuden sukupolven langattomat verkot ja mobiili- sek\u00e4 IoT-sovellukset ovat jalkautumassa aina kriittisen infrastruktuurin j\u00e4rjestelmiin asti. Fyysisen ja digitaalisen maailman rajapinnassa osana IoT-sovelluksia toimivat langattomat sensoriverkot ovat alttiita laajalle kirjolle erilaisia tietoturvauhkia niiden avoimen luonteen, IoT-sovellusten teknologisen kypsym\u00e4tt\u00f6myyden ja alati kehittyv\u00e4n kyberrikollisuuden vuoksi. Langattomien sensoriverkkojen suojaaminen kyberhy\u00f6kk\u00e4yksilt\u00e4 ja muulta niiden luotettavaa toimintakyky\u00e4 uhkaavalta ja vahingoittavalta toiminnalta on t\u00e4rke\u00e4 tutkimusaihe. T\u00e4ss\u00e4 ty\u00f6ss\u00e4 tutkittiin hiljattain julkaistun esineiden internetin sovellusymp\u00e4rist\u00f6\u00e4 j\u00e4ljittelev\u00e4n Bot-IoT -datajoukon avulla verkkohy\u00f6kk\u00e4yksien tunnistamista anomalioiden havaitsemisen keinoin k\u00e4ytt\u00e4m\u00e4ll\u00e4 moderneja syv\u00e4oppimismenetelmi\u00e4. Ty\u00f6ss\u00e4 implementoidaan ja vertaillaan nelj\u00e4\u00e4 autoenkooderiarkkitehtuuriin perustuvaa yksinkertaista ja laskennallisesti kevytt\u00e4 syv\u00e4oppimismallia. Suorituskykyisin toistuvaan neuroverkkoon perustuva LSTM-autoenkooderi kykeni tunnistamaan yli 3,6 miljoonaa hy\u00f6kk\u00e4yst\u00e4 j\u00e4tt\u00e4en vain 101 hy\u00f6kk\u00e4yst\u00e4 tunnistamatta. Ty\u00f6ss\u00e4 tehdyn kaltaista tutkimusta Bot-IoT -datajoukkoon ei ole tiedeyhteis\u00f6ss\u00e4 aiemmin toteutettu eik\u00e4 vastaavia tuloksia ole ennen saatu. Lis\u00e4ksi ty\u00f6ss\u00e4 annetaan kattava teoreettinen tausta tunnetuimmista syv\u00e4oppimismenetelmist\u00e4 ja niiden soveltamisesta anomalioiden havaitsemiseen.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The next-generation wireless and mobile networking as well as IoT applications accounting for an ever-increasing share of the global IP network traffic are being widely deployed reaching critical infrastructures. Acting as an interface between the physical and the digital world in IoT applications, wireless sensor networks are exposed to a wide range of information security threats due to their open nature of communications, the technological immaturity of IoT solutions and the accelerating growth of cybercrime. Protecting wireless sensor networks from cyberattacks and other factors that may impair the continuity of their secure and reliable operations is an important area of research. In this thesis, the ability of detecting network attacks with methods based on deep learning using principles from anomaly detection was investigated by a recently published dataset called Bot-IoT that incorporates flow-based network traffic from an IoT environment. Four different lightweight deep learning based autoencoders were implemented for evaluation and comparison purposes. The results demonstrated the superiority of the recurrent LSTM-autoencoder model by detecting over 3.6 million attacks while leaving only 101 attacks undetected. The empirical study conducted in this thesis with the Bot-IoT -dataset is the first of its kind in the scientific community and similar results have not yet been published. In addition, a comprehensive theoretical background of the most common deep learning methods and their applicability to anomaly detection is given.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Miia Hakanen (mihakane@jyu.fi) on 2019-12-13T12:56:55Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-12-13T12:56:55Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "112", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "anomalian havaitseminen", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "langaton sensoriverkko", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "syv\u00e4oppiminen", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "autoenkooderi", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Anomalioiden havaitseminen langattomissa sensoriverkoissa syv\u00e4oppimisen avulla", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201912135267", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "sensoriverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neuroverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "langattomat verkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "esineiden internet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "hy\u00f6kk\u00e4ys", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "tietoturva", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
|