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dc.contributor.author
Kiachidis, Ioannis
en
dc.date.accessioned
2021-08-26T10:51:59Z
dc.date.issued
2021-08-26
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29738
dc.rights
Default License
dc.subject
Malware analysis
en
dc.title
Recent Trends in Malware Analysis
en
heal.type
masterThesis
en_US
heal.creatorID.email
y.kiahidis@disr.it
heal.creatorID.email
yiannis.kiachidis@pm.me
heal.contributorID.email
y.kiahidis@disr.it
heal.contributorID.email
yiannis.kiachidis@pm.me
heal.dateAvailable
2022-08-25T21:00:00Z
heal.language
en
en_US
heal.access
embargo
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.references
C. Q. Nguyen and J. E. Goldman, “Malware Analysis Reverse Engineering (MARE) methodology & Malware Defense (M.D.) timeline,” 2010, doi: 10.1145/1940941.1940944
en_US
heal.references
J. B. Higuera, C. A. Aramburu, J. R. B. Higuera, M. A. S. Urban, and J. A. S. Montalvo, “Systematic approach to Malware analysis (SAMA),” Appl. Sci., 2020, doi: 10.3390/app10041360
en_US
heal.references
O. Or-Meir, N. Nissim, Y. Elovici, and L. Rokach, “Dynamic malware analysis in the modern era—A state of the art survey,” ACM Comput. Surv., 2019, doi: 10.1145/3329786
en_US
heal.references
D. Gibert, C. Mateu, and J. Planes, “The rise of machine learning for detection and classification of malware: Research developments, trends and challenges,” Journal of Network and Computer Applications. 2020, doi: 10.1016/j.jnca.2019.102526
en_US
heal.references
M. Sikorski and A. Honig, Practical malware analysis: the hands-on guide to dissecting malicious software. no starch press. 2012
en_US
heal.references
M. Simonsen, “Multinomial malware classification using control flow graphs,” no. June, 2019
en_US
heal.references
A. Verma, M. Rao, A. Gupta, W. Jeberson, and V. Singh, “a Literature Review on Malware and Its Analysis,” Int J Cur Res Rev, 2013
en_US
heal.references
M. K.A., Learning Malware Analysis. 2018
en_US
heal.fileFormat
PDF
en_US
heal.recordProvider
School of Science and Technology, MSc in Cybersecurity
en_US
heal.publicationDate
2021-05-12
heal.bibliographicCitation
Ioannis Kiachidis, "Recent Trends in Malware Analysis", School of Science & Technology/MSc in Cybersecurity, International Hellenic University, 2021.
en
heal.abstract
This dissertation was written as a part of the MSc in Cybersecurity at the International Hellenic University. Here goes a summary of the dissertation. This dissertation presents an extensive overview of the malware analysis world by the time of writing. Specifically, questions of what is the current of cybercrime and where malware stands are answered in the beginning. Then the objectives of malware analysis are enumerated, its characteristics presented and base definition are explained (e.g. analysis, detection, classification). General classic approaches (static and dynamic analysis) are unfolded and their strengths and weaknesses are discussed. Threats are reviewed and their characteristics are analyzed (malware types, APTs, malware self-preservation/protection mechanisms). Emerging and established modern approaches in analysis and complementary procedures like automated analysis, memory analysis or hybrid analysis, are presented. Also, the emerging applications of machine learning in malware analysis are reviewed. Then the focus of the dissertation is cast upon the presentation and details unfolding of the structured malware analysis methodologies (MARE, SAMA). The modern SAMA methodology in implemented and at the same time it is compared to the existing MARE methodology. The dissertation closes with conclusions and notes on the state of modern malware analysis based on the topics discussed.
en
heal.advisorName
Baltatzis, Dimitrios
en
heal.committeeMemberName
Baltatzis, Dimitrios
en
heal.committeeMemberName
Serketzis, Nikolaos
en
heal.committeeMemberName
Chatzimisios, Periklis
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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