Addressing user perception and implementing Hedera Hashgraph and voice recognition into Multi-Factor Authentication (MFA) system
Wong Jie Sheng, Intan Farahana Kasmin, Salmiah Mohamad Amin, Nur Khairunnisha Zainal · International journal of data science and advanced analytics. · 2023
Multi-Factor Authentication (MFA) has been gaining popularity in recent years for offering extra layers of protection to secure user accounts. Most of the integration of MFA today includes a OTP to be sent through a Short Message Service (SMS) identified by a user’s phone number or through an MFA application. However, MFA executions like these still possess underlying vulnerabilities like phishing attacks. The aim of this research is to propose an effective Software Engineering solution to decrease the number of successful attacks on user accounts during the MFA process. The proposed system suggests a first-level authentication in the form of textbox for users to input their current location of access. This can prompt the user naturally to always pay attention to the user information provided before they continue on with the MFA process. This can help users to identify a threat and realise an attempted phishing attack before giving the full access to the attackers if proceeded with the MFA process. This paper proposes a solution that takes in voice recognition biometric alongside the traditional OTP. An improved distributed ledger technology in the form of Hedera Hashgraph is also proposed which addresses efficiency and security shortcomings of Blockchains like the Ethereum Blockchain. For this research, self-selection sampling was carried out and 5 people were chosen to attend a one-to-one online interview session.