Improving Classification of Malware Families using Learning a Distance Metric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00347213" target="_blank" >RIV/68407700:21240/21:00347213 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.insticc.org/node/TechnicalProgram/icissp/2021/presentationDetails/103263" target="_blank" >https://www.insticc.org/node/TechnicalProgram/icissp/2021/presentationDetails/103263</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010326306430652" target="_blank" >10.5220/0010326306430652</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Classification of Malware Families using Learning a Distance Metric

Popis výsledku v původním jazyce

The objective of malware family classification is to assign a tested sample to the correct malware family. This paper concerns the application of selected state-of-the-art distance metric learning techniques to malware families classification. The goal of distance metric learning algorithms is to find the most appropriate distance metric parameters concerning some optimization criteria. The distance metric learning algorithms considered in our research learn from metadata, mostly contained in the headers of executable files in the PE file format. Several experiments have been conducted on the dataset with 14,000 samples consisting of six prevalent malware families and benign files. The experimental results showed that the average precision and recall of the k-Nearest Neighbors algorithm using the distance learned on training data were improved significantly comparing when the non-learned distance was used. The k-Nearest Neighbors classifier using the Mahalanobis distance metric learned by the Metric Learning for Kernel Regression method achieved average precision and recall, both of 97.04% compared to Random Forest with a 96.44% of average precision and 96.41% of average recall, which achieved the best classification results among the state-of-the-art ML algorithms considered in our experiments.

Název v anglickém jazyce

Improving Classification of Malware Families using Learning a Distance Metric

Popis výsledku anglicky

The objective of malware family classification is to assign a tested sample to the correct malware family. This paper concerns the application of selected state-of-the-art distance metric learning techniques to malware families classification. The goal of distance metric learning algorithms is to find the most appropriate distance metric parameters concerning some optimization criteria. The distance metric learning algorithms considered in our research learn from metadata, mostly contained in the headers of executable files in the PE file format. Several experiments have been conducted on the dataset with 14,000 samples consisting of six prevalent malware families and benign files. The experimental results showed that the average precision and recall of the k-Nearest Neighbors algorithm using the distance learned on training data were improved significantly comparing when the non-learned distance was used. The k-Nearest Neighbors classifier using the Mahalanobis distance metric learned by the Metric Learning for Kernel Regression method achieved average precision and recall, both of 97.04% compared to Random Forest with a 96.44% of average precision and 96.41% of average recall, which achieved the best classification results among the state-of-the-art ML algorithms considered in our experiments.

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 7th International Conference on Information Systems Security and Privacy

ISBN

978-989-758-491-6

ISSN

2184-4356

e-ISSN

—

Počet stran výsledku

10

Strana od-do

643-652

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Vídeň / Virtuální

Datum konání akce

11. 2. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000664076200068