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Author:Maaranen, Hannu
Title:Data file classification using statistical analysis
Tiedoston luokittelu tilastollisella analyysillä
Publication type:Master's thesis
Publication year:2009
Pages:(12+) 70      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Tietämystekniikka   (T-93)
Supervisor:Syrjänen, Markku
Instructor:Ståhlberg, Mika
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions

Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

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Opening a thesis

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    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

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Printing the thesis

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  • Other customers can use the printer u90203-psc3. All printing is subject to a charge to non-University members.
Location:P1 Ark T80     | Archive
Keywords:n-gram
statistical analysis
Bloom
malware
signature
detection
fingerprint
virus
zero-day
anomaly
automatic
Markov
n-grammi
tilastollinen analyysi
Bloom
haittaohjelma
tunniste
sormenjälki
virus
tunnistamaton
poikkeama
automaattinen
Markov
Abstract (fin): Vertailen kolmea n-grammeihin pohjautuvaa tilastollista menetelmää, joilla pystytään luokittelemaan tiedostonäytteitä automatisoidusti ennaltamääriteltyihin ryhmiin havaittujen tilastollisten poikkeamien perusteella.
Esittelen myös miten näitä menetelmiä pystytään soveltamaan perinteisten virus-tunnisteiden tapaan tunnistamaan saastuneita tiedostoja ja toimimaan osana yleistä tiedostonäytteiden hallintaprosessia näytteiden analysoinnin tukena luomassa tietoturvaa asiakkaalle.
Käyn myös läpi muita yleisiä menetelmiä haittaohjelmien tunnistamiseen ja torjuntaan, sekä luon katsauksen tilastollisten menetelmien soveltuvuuteen laajemmin haittaohjelmien analysoinnin ja torjunnan näkökulmasta.
ED:2009-10-26
INSSI record number: 38503
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