Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00341833" target="_blank" >RIV/68407700:21240/20:00341833 - isvavai.cz</a>
Result on the web
<a href="https://www.insticc.org/node/TechnicalProgram/icissp/2020/presentationDetails/91808" target="_blank" >https://www.insticc.org/node/TechnicalProgram/icissp/2020/presentationDetails/91808</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0009180807250732" target="_blank" >10.5220/0009180807250732</a>
Alternative languages
Result language
angličtina
Original language name
Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection
Original language description
Distance metric learning is concerned with finding appropriate parameters of distance function with respect to a particular task. In this work, we present a malware detection system based on static analysis. We use k-nearest neighbors (KNN) classifier with weighted heterogeneous distance function that can handle nominal and numeric features extracted from portable executable file format. Our proposed approach attempts to specify the weights of the features using particle swarm optimization algorithm. The experimental results indicate that KNN with the weighted distance function improves classification accuracy significantly.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 6th International Conference on Information Systems Security and Privacy
ISBN
978-989-758-399-5
ISSN
2184-4356
e-ISSN
—
Number of pages
8
Pages from-to
725-732
Publisher name
SciTePress
Place of publication
Madeira
Event location
Valletta
Event date
Feb 25, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000570766300079