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[{"key": "dc.contributor.advisor", "value": "Lehto, Martti", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Voutilainen, Janne", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-08-14T06:25:04Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-08-14T06:25:04Z", "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/123456789/65229", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Vihamieliseen kyberilmi\u00f6\u00f6n viittavan indikaation l\u00f6yt\u00e4minen avoimista l\u00e4hteist\u00e4 on vaativa teht\u00e4v\u00e4. Tieto, jota strateginen kybertiedustelu tuottaa, mahdollistaa suurten yritysten varautumisen kyberhy\u00f6kkayksiin. Tutkimuksessa vastataan kysymykseen: Voidaanko koneoppimista hy\u00f6dynt\u00e4\u00e4 strategisessa avoimen\nl\u00e4hteiden kybertiedustelussa?\nVuonna 2019 kyberrikolliset alkoivat k\u00e4ytt\u00e4\u00e4 uutta taktiikkaa, jossa he vaativat suuria rahasummia yrityksilt\u00e4 k\u00e4ytt\u00e4m\u00e4ll\u00e4 kiristyshaittaohjelmia. Ilmi\u00f6n\nnimi on Big Game Hunting. Tutkimuksessa ilmi\u00f6t\u00e4 k\u00e4ytettiin strategisen kybertiedustelun esimerkkikohteena.\nTutkimustulokset saavutettiin suunnittelututkimuksella. Tutkimuksessa\ntehtiin kaksi suunnittelututkimuksen kierrosta. Ensimm\u00e4isen kierroksen tuloksena syntyi koneoppimismalli, joka suunniteltiin tiedusteluohjauksen mukaisesti.\nKyberturvallisuuskeskus antoi rajoitetun datan, josta mallilla etsittiin tietoa Big\nGame Hunting ilmi\u00f6st\u00e4. Malli kykeni l\u00f6yt\u00e4m\u00e4\u00e4n tietoa, mutta tiedusteluohjauksen kannalta tieto oli riitt\u00e4m\u00e4t\u00f6nt\u00e4. Toisen kierroksen tuloksena syntyneess\u00e4 ratkaisussa tietoa haettiin IBM Watson Discovery News tietokannasta. Haut tuottivat riitt\u00e4v\u00e4sti tiedustelutietoa ilmi\u00f6st\u00e4.\nKun koneoppimen ja tiedusteluprosessi yhdistettiin, t\u00e4rkeimm\u00e4t havainnot\nolivat, ett\u00e4 oikeanlaiset kyselyt tuottavat parhaan tiedon tiedonker\u00e4ykseen. Lisaksi lyhyet Watson-algoritmin tuottamat virkkeet osoittautuivat hy\u00f6dyllisiksi.\nKoneoppiminen helpotti tiedon prosessointia luomalla ohjaamattomalla oppimisella dokumentteihin metatietoa, jonka perusteella tieto jaettiin sopiviin kokonaisuuksiin. Kokonaisuudet mahdollistavat tiedon analysoinnin ja uuden tiedon\nl\u00f6yt\u00e4misen. Tutkimuksen johtop\u00e4\u00e4t\u00f6ksen\u00e4 voidaan todeta, ett\u00e4 koneoppimista\nvoidaan hy\u00f6dynt\u00e4\u00e4 strategisessa avointen l\u00e4hteiden kybertiedustelussa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Finding an indication from open sources to reveal a malicious cyber phenomenon\nis a demanding task. The information that is produced from the strategic cyber\nintelligence processes with, large-scale organizations can better prepare for\ncyber-attacks. The study aims to answer the question: Can Machine Learning\n(ML) be utilized for strategic open source cyber intelligence.\nIn 2019, e-criminals have adopted new tactics to demand enormous ransoms\nin bitcoins from large-scale organizations by using malicious ransomware\nsoftware. The phenomenon is called Big Game Hunting. In the study, Big Game\nHunting was used as an example for a target that was investigated with strategic\ncyber intelligence.\nThe answers to the research questions were achieved with The Design\nScience Research Process. The Design Science Cycle was conducted two times. In\nthe first solution, a custom ML model was created precisely for the intelligence\ndirection. The queried data was a limited dataset that was provided by the\nNational Cyber Security Centre of Finland. The model returned correct data, but\nin the perspective of intelligence direction, the information was insufficient. In\nthe second solution, the queries were made from the IBM Watson Discovery\nNews data-set. The results offered enough valuable intelligence information\nabout Big Game Hunting.\nWhen the intelligence cycle and ML were combined, the main findings were\nthat in information collection, the correct queries offered the best information.\nFurthermore, the short sentences, passages created by the Watson algorithm in\nthe first solution proved to be useful. In information procession with unsupervised\nlearning, the Watson algorithm was able to label the data in entities. The\nentities enabled the ability to analyse the data and find new, hidden information.\nThe conclusion from the research was that ML could be utilised in strategic cyber\nintelligence.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Miia Hakanen (mihakane@jyu.fi) on 2019-08-14T06:25:04Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-08-14T06:25:04Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "63", "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": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Machine learning and intelligence cycle : enhancing the cyber intelligence process", "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-201908143828", "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": "Tietojenk\u00e4sittelytiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Computer Science", "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": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "tiedustelu", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kyberturvallisuus", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "intelligence and reconnaissance", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "cyber security", "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"}]
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