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Random Forest in Whitelist-Based ATM Security
Intelligent Information and Database Systems, Aciids 2022, Pt Ii ; 13758:292-301, 2022.
Article in English | Web of Science | ID: covidwho-2243050
ABSTRACT
Accelerated by the COVID-19 pandemic, the trend of highly-sophisticated logical attacks on Automated Teller Machines (ATMs) is ever-increasing nowadays. Due to the nature of attacks, it is common to use zero-day protection for the devices. The most secure solutions available are using whitelist-based policies, which are extremely hard to configure. This article presents the concept of a semi-supervised decision support system based on the Random forest algorithm for generating a whitelist-based security policy using the ATM usage data. The obtained results confirm that the Random forest algorithm is effective in such scenarios and can be used to increase the security of the ATMs.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Randomized controlled trials Language: English Journal: Intelligent Information and Database Systems, Aciids 2022, Pt Ii Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Randomized controlled trials Language: English Journal: Intelligent Information and Database Systems, Aciids 2022, Pt Ii Year: 2022 Document Type: Article