Proof of Stake And Proof of Work Approach for Malware Detection Technologies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25840886%3A_____%2F22%3AN0000006" target="_blank" >RIV/25840886:_____/22:N0000006 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3156/paper33.pdf" target="_blank" >https://ceur-ws.org/Vol-3156/paper33.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Proof of Stake And Proof of Work Approach for Malware Detection Technologies

Popis výsledku v původním jazyce

Malware detection is critical given the rapid spread of malware on the Internet as it functions as an early warning system for computer security against malware and cyberattack. This keeps hackers away from the computer and prevents information from being compromised. Existing antivirus software and hardware are unable to effectively detect new or modified old classes of viruses, and are prone to a large number of false positives. Therefore, the problem of malware detection requires an immediate solution to ensure the safe use of the network. Thus, there is a need to develop new methods of analyzing potentially dangerous code in order to detect malicious software. To solve this problem, a Proof of stake and Proof of work approach for malware detection technologies based on the use of Blockchain technology was developed. A mechanism has been implemented to remove features that may indicate that a potentially dangerous code belongs to a certain class of malware, as well as a mechanism that analyzes potentially dangerous code, carried out in parallel by different network participants using Proof of work. By using the concept of Proof of work, the developed method provides accelerated analysis of potentially dangerous codes. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The application of the developed approach makes it possible to detect malicious software of different classes with an accuracy of 98.81- 99.33%.

Název v anglickém jazyce

Proof of Stake And Proof of Work Approach for Malware Detection Technologies

Popis výsledku anglicky

Malware detection is critical given the rapid spread of malware on the Internet as it functions as an early warning system for computer security against malware and cyberattack. This keeps hackers away from the computer and prevents information from being compromised. Existing antivirus software and hardware are unable to effectively detect new or modified old classes of viruses, and are prone to a large number of false positives. Therefore, the problem of malware detection requires an immediate solution to ensure the safe use of the network. Thus, there is a need to develop new methods of analyzing potentially dangerous code in order to detect malicious software. To solve this problem, a Proof of stake and Proof of work approach for malware detection technologies based on the use of Blockchain technology was developed. A mechanism has been implemented to remove features that may indicate that a potentially dangerous code belongs to a certain class of malware, as well as a mechanism that analyzes potentially dangerous code, carried out in parallel by different network participants using Proof of work. By using the concept of Proof of work, the developed method provides accelerated analysis of potentially dangerous codes. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The use of the concept of Proof of stake provides an opportunity to increase the accuracy of malware detection by validating the results of the participant's analysis, taking into account the coefficient of efficiency of the participant's computing resources by the method of soft voting. In the key of using blockchain technology, validation provides an opportunity to prevent the use of analysis results from a potentially compromised participant. The application of the developed approach makes it possible to detect malicious software of different classes with an accuracy of 98.81- 99.33%.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

3rd International Workshop on Intelligent Information Technologies and Systems of Information Security, IntelITSIS 2022

ISBN

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ISSN

16130073

e-ISSN

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Počet stran výsledku

10

Strana od-do

432-441

Název nakladatele

CEUR-WS

Místo vydání

Khmelnytskyi

Místo konání akce

Khmelnytskyi

Datum konání akce

23. 3. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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