Transforming Weakness into Strength: Improving Unreliable
Malware Detection Methods

J 2024

Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods

NOVÁK, Pavel a Václav OUJEZSKÝ

Základní údaje

Originální název

Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods

Autoři

NOVÁK, Pavel a Václav OUJEZSKÝ

Vydání

Journal of Communications Software and Systems, Croatia, University of Split, 2024, 1845-6421

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

20203 Telecommunications

Stát vydavatele

Chorvatsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 0.700

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/24:00139903

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

behavioral analysis; Cybersecurity; Malware Detection; Sequence Similarity

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2025 12:37, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

VK01030030, projekt VaV

Název: Systém pro zálohování a ukládání dat s integrovanou aktivní ochranou proti kybernetickým hrozbám

Investor: Ministerstvo vnitra ČR, Systém pro zálohování a ukládání dat s integrovanou aktivní ochranou proti kybernetickým hrozbám

Citovat

NOVÁK, Pavel a Václav OUJEZSKÝ. Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods. Journal of Communications Software and Systems. Croatia: University of Split, 2024, roč. 20, č. 4, s. 317-328. ISSN 1845-6421. Dostupné z: https://doi.org/10.24138/jcomss-2024-0098.

@article{2460819,
   author = {Novák, Pavel and Oujezský, Václav},
   article_location = {Croatia},
   article_number = {4},
   doi = {https://doi.org/10.24138/jcomss-2024-0098},
   keywords = {behavioral analysis; Cybersecurity; Malware Detection; Sequence Similarity},
   language = {eng},
   issn = {1845-6421},
   journal = {Journal of Communications Software and Systems},
   title = {Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods},
   url = {https://doi.org/https://doi.org/10.24138/jcomss-2024-0098},
   volume = {20},
   year = {2024}
}

TY  - JOUR
ID  - 2460819
AU  - Novák, Pavel - Oujezský, Václav
PY  - 2024
TI  - Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods
JF  - Journal of Communications Software and Systems
VL  - 20
IS  - 4
SP  - 317-328
EP  - 317-328
PB  - University of Split
SN  - 18456421
KW  - behavioral analysis
KW  - Cybersecurity
KW  - Malware Detection
KW  - Sequence Similarity
UR  - https://doi.org/https://doi.org/10.24138/jcomss-2024-0098
N2  - 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.
ER  -

NOVÁK, Pavel a Václav OUJEZSKÝ. Transforming Weakness into Strength: Improving Unreliable Malware Detection Methods. \textit{Journal of Communications Software and Systems}. Croatia: University of Split, 2024, roč.~20, č.~4, s.~317-328. ISSN~1845-6421. Dostupné z: https://doi.org/10.24138/jcomss-2024-0098.

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