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Analysis of malicious campaigns in multiple heterogeneous threat datasources

Michał Kruczkowski

Abstract

This doctoral dissertation concerns the problems of identification of malware (malicious software) campaigns on the Internet. This is an extremely important issue because it arises from a real need to ensure safety in rapidly growing computer networks. Proposed approach assumes the use of data mining and machine learning methods. It utilizes data about threat incidents t aken from multiple datasources related with v arious layers of ISO/OSI network communication model. The r esults of the doctoral dissertation confirm that the automated analysis and classification of data from heterogeneous datasources can be efficiently used to protect the Internet. The use of full info rmation about threats, including v arious network layers, leads to achieve better results compared with frequently used single layer analysis. Data mining and machine learning methods can efficiently support the cybercrime protection systems. The system MalCAS (Malware Campaign Analysis System) for malware campaigns identification that implements these methods fully corresponds the demands of contemporary networks. It can be applied as a useful and powerful tool to support the software environment ensuring net work cybersecurity
Record ID
WUT16c6df045a4c4214bf32dcac181b35d1
Diploma type
Doctor of Philosophy
Author
Michał Kruczkowski Michał Kruczkowski,, Undefined Affiliation
Title in Polish
Analiza złośliwych kampanii na podstawie danych o atakach pozyskiwanych z heterogenicznych zródeł
Title in English
Analysis of malicious campaigns in multiple heterogeneous threat datasources
Language
(pl) Polish
Certifying University/Institution (when outside WUT)
Systems Research Institute (IBS PAN) [Polish Academy of Sciences (PAN)]
Discipline
automation and robotics / (technology domain) / (technological sciences)
Status
Finished
Defense Date
20-11-2015
Title date
20-11-2015
Supervisor
External reviewers
Marek Amanowicz Marek Amanowicz,, Undefined Affiliation
Joanna Kołodziej Joanna Kołodziej,, Undefined Affiliation
Keywords in English
x
Abstract in English
This doctoral dissertation concerns the problems of identification of malware (malicious software) campaigns on the Internet. This is an extremely important issue because it arises from a real need to ensure safety in rapidly growing computer networks. Proposed approach assumes the use of data mining and machine learning methods. It utilizes data about threat incidents t aken from multiple datasources related with v arious layers of ISO/OSI network communication model. The r esults of the doctoral dissertation confirm that the automated analysis and classification of data from heterogeneous datasources can be efficiently used to protect the Internet. The use of full info rmation about threats, including v arious network layers, leads to achieve better results compared with frequently used single layer analysis. Data mining and machine learning methods can efficiently support the cybercrime protection systems. The system MalCAS (Malware Campaign Analysis System) for malware campaigns identification that implements these methods fully corresponds the demands of contemporary networks. It can be applied as a useful and powerful tool to support the software environment ensuring net work cybersecurity

Uniform Resource Identifier
https://repo.pw.edu.pl/info/phd/WUT16c6df045a4c4214bf32dcac181b35d1/
URN
urn:pw-repo:WUT16c6df045a4c4214bf32dcac181b35d1

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