Parallel Instance Filtering for Malware Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00361618" target="_blank" >RIV/68407700:21240/22:00361618 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10011504" target="_blank" >https://ieeexplore.ieee.org/document/10011504</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SEAA56994.2022.00012" target="_blank" >10.1109/SEAA56994.2022.00012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parallel Instance Filtering for Malware Detection

Popis výsledku v původním jazyce

Machine learning algorithms are widely used in the area of malware detection. With the growth of sample amounts, training of classification algorithms becomes more and more expensive. In addition, training data sets may contain redundant or noisy instances. The problem to be solved is how to select representative instances from large training data sets without reducing the accuracy. This work presents a new parallel instance selection algorithm called Parallel Instance Filtering (PIF). The main idea of the algorithm is to split the data set into non-overlapping subsets of instances covering the whole data set and apply a filtering process for each subset. Each subset consists of instances that have the same nearest enemy. As a result, the PIF algorithm is fast since subsets are processed independently of each other using parallel computation. We compare the PIF algorithm with several state-of-the-art instance selection algorithms on a large data set of 500,000 malicious and benign samples. The feature set was extracted using static analysis, and it includes metadata from the portable executable file format. Our experimental results demonstrate that the proposed instance selection algorithm reduces the size of a training data set significantly with the only slightly decreased accuracy. The PIF algorithm outperforms existing instance selection methods used in the experiments in terms of the ratio between average classification accuracy and storage percentage.

Název v anglickém jazyce

Parallel Instance Filtering for Malware Detection

Popis výsledku anglicky

Machine learning algorithms are widely used in the area of malware detection. With the growth of sample amounts, training of classification algorithms becomes more and more expensive. In addition, training data sets may contain redundant or noisy instances. The problem to be solved is how to select representative instances from large training data sets without reducing the accuracy. This work presents a new parallel instance selection algorithm called Parallel Instance Filtering (PIF). The main idea of the algorithm is to split the data set into non-overlapping subsets of instances covering the whole data set and apply a filtering process for each subset. Each subset consists of instances that have the same nearest enemy. As a result, the PIF algorithm is fast since subsets are processed independently of each other using parallel computation. We compare the PIF algorithm with several state-of-the-art instance selection algorithms on a large data set of 500,000 malicious and benign samples. The feature set was extracted using static analysis, and it includes metadata from the portable executable file format. Our experimental results demonstrate that the proposed instance selection algorithm reduces the size of a training data set significantly with the only slightly decreased accuracy. The PIF algorithm outperforms existing instance selection methods used in the experiments in terms of the ratio between average classification accuracy and storage percentage.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2022 48th Euromicro Conference on Software Engineering and Advanced Applications

ISBN

978-1-6654-6152-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

13-20

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Maspalomas, Gran Canaria

Datum konání akce

31. 8. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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