Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00139903" target="_blank" >RIV/00216224:14330/24:00139903 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/https://doi.org/10.24138/jcomss-2024-0098" target="_blank" >https://doi.org/https://doi.org/10.24138/jcomss-2024-0098</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24138/jcomss-2024-0098" target="_blank" >10.24138/jcomss-2024-0098</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods

Popis výsledku v původním jazyce

This paper proposes a novel malware detection methodology that leverages unreliable Indicators of Compromise to enhance the identification of latent malware. The core contribution lies in introducing a sequence-based detection method that contextualizes unreliable IoCs to improve accuracy and reduce false positives. Unlike traditional methods reliant on predefined signatures or behavior analysis, this approach dynamically assesses system behaviors, focusing on suspicious actions and interaction patterns. Key contributions include a novel combination of unreliable IoCs with sequence alignment methods, an extensive mapping study of detection techniques, and initial experiments on a dataset of over 19,000 malware samples. Results demonstrate the method’s ability to cluster and identify malware families based on their behavioral signatures, even in its early developmental stage. This innovative approach shows promise for detecting previously unknown threats, establishing a foundation for advanced research in malware detection.

Název v anglickém jazyce

Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods

Popis výsledku anglicky

This paper proposes a novel malware detection methodology that leverages unreliable Indicators of Compromise to enhance the identification of latent malware. The core contribution lies in introducing a sequence-based detection method that contextualizes unreliable IoCs to improve accuracy and reduce false positives. Unlike traditional methods reliant on predefined signatures or behavior analysis, this approach dynamically assesses system behaviors, focusing on suspicious actions and interaction patterns. Key contributions include a novel combination of unreliable IoCs with sequence alignment methods, an extensive mapping study of detection techniques, and initial experiments on a dataset of over 19,000 malware samples. Results demonstrate the method’s ability to cluster and identify malware families based on their behavioral signatures, even in its early developmental stage. This innovative approach shows promise for detecting previously unknown threats, establishing a foundation for advanced research in malware detection.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01030030" target="_blank" >VK01030030: Systém pro zálohování a ukládání dat s integrovanou aktivní ochranou proti kybernetickým hrozbám</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Communications Software and Systems

ISSN

1845-6421

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

HR - Chorvatská republika

Počet stran výsledku

12

Strana od-do

317-328

Kód UT WoS článku

001395105000001

EID výsledku v databázi Scopus

2-s2.0-85213533188