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[{"key": "dc.contributor.advisor", "value": "Terziyan, Vagan", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Verma, Dhruv", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-05-27T08:52:09Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-05-27T08:52:09Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/95217", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This thesis explores how artificial intelligence (AI) techniques and machine learning (ML) algorithms can enhance adaptive anomaly detection frameworks while aiming to develop effective strategies for identifying and mitigating evolving cyberthreats. The main objective of this study is to create a unified framework that reduces manual inter-vention, minimizes false positives, and offers a robust and resilient approach to threat mitigation. \nTo collect datasets for analyzing the behavior of cyberthreats, a number of cyber-attack simulations, including threats such as - malware, data breaches, and SQL injections, were created. Additionally, malicious datasets from Kaggle were utilized to provide a larger amount of data. After normalizing the datasets, several ML algorithms were utilized to train the data and establish a threshold mechanism. This mechanism dynamically adjusts parameters corresponding to specific cyberthreats, ensuring accurate identification and mitigation. This study demonstrates how modern AI techniques can transform anomaly detection, making it more effective, time-efficient, and resource-friendly.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4m\u00e4 opinn\u00e4ytety\u00f6 tutkii, kuinka teko\u00e4lytekniikat (AI) ja koneoppimisalgoritmit voivat parantaa adaptiivisia poikkeamien havaitsemiskehyksi\u00e4 ja pyrki\u00e4 kehitt\u00e4m\u00e4\u00e4n tehokkaita strategioita kehittyvien kyberuhkien tunnistamiseen ja lievent\u00e4miseen. T\u00e4m\u00e4n tutkimuksen p\u00e4\u00e4tavoitteena on luoda yhten\u00e4inen viitekehys, joka v\u00e4hent\u00e4\u00e4 manuaalisia toimenpiteit\u00e4, minimoi v\u00e4\u00e4ri\u00e4 positiivisia tuloksia ja tarjoaa vankan ja kest\u00e4v\u00e4n l\u00e4hestymistavan uhkien lievent\u00e4miseen.\nTietojen ker\u00e4\u00e4miseksi kyberuhkien k\u00e4ytt\u00e4ytymisen analysointia varten luotiin useita ky-berhy\u00f6kk\u00e4yssimulaatioita, mukaan lukien uhkia, kuten - haittaohjelmat, tietomurrot ja SQL-injektiot. Lis\u00e4ksi Kagglen haitallisia tietojoukkoja hy\u00f6dynnettiin suuremman tietom\u00e4\u00e4r\u00e4n tuottamiseen. Datajoukkojen normalisoinnin j\u00e4lkeen k\u00e4ytettiin useita ML-algoritmeja tietojen kouluttamiseen ja kynnysmekanismin luomiseen. T\u00e4m\u00e4 mekanismi s\u00e4\u00e4t\u00e4\u00e4 dynaamisesti tiettyj\u00e4 kyberuhkia vastaavia parametreja varmistaen tarkan tunnis-tamisen ja lievent\u00e4misen. T\u00e4m\u00e4 tutkimus osoittaa, kuinka nykyaikaiset teko\u00e4lytekniikat voivat muuttaa poikkeamien havaitsemista tehden siit\u00e4 tehokkaamman, aikatehokkaam-man ja resursseja s\u00e4\u00e4st\u00e4v\u00e4mm\u00e4n.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2024-05-27T08:52:09Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-05-27T08:52:09Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "70", "language": null, "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": "CC BY 4.0", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Enhancing Cybersecurity Through Adaptive Anomaly Detection\tUsing Modern AI Techniques", "language": null, "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-202405273981", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "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": "Artificial Intelligence", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "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.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://creativecommons.org/licenses/by/4.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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