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Evaluating the Impact of Multi Factor Authentication on Cybersecurity Effectiveness

Ranga Premsai,

Maryland, USA,

Premsairanga809@gmail.com.

Abstract—

In today's digital age, the convenience of online financial transactions is accompanied by rising cybersecurity risks. The shift to digital platforms for conducting daily financial activities has significantly exposed customers to cyber threats. These threats often result from unauthorized access, phishing attacks, and fraud attempts by cybercriminals. Ensuring the security of these transactions is crucial to maintain users' trust and to protect sensitive financial data. This study proposes a comprehensive security framework to strengthen the safety of online financial transactions, using a combination of multi-factor authentication (MFA) and a machine learning-based fraud detection system, termed "E-cyberboost." The framework operates on two primary layers, each designed to address a different aspect of security: identity verification and real-time fraud detection. The first layer involves multi-factor authentication, where users must verify their identity. This layer typically combines something the user knows (like a password) with something they have (such as a one-time password or OTP sent to their mobile device) or something they are (such as fingerprint or facial recognition). MFA significantly reduces unauthorized access by ensuring that even if one authentication factor is compromised, additional verification is required to gain access. The second layer introduces an advanced machine-learning component. Named "E-cyberboost," this machine learning model actively monitors ongoing transactions, detecting patterns that may indicate fraudulent activity. If the system identifies any unusual or suspicious transaction behaviour, it immediately triggers a security response, such as additional user verification or temporarily blocking the transaction. This proactive approach allows the system to address potential fraud in real time, adapting to evolving threats by learning from previous incidents and continuously improving its detection accuracy. By combining the preventive measures of MFA with the responsive capabilities of machine learning, this framework provides robust security against both common and sophisticated cyber threats. The study discusses the methodology, implementation, and effectiveness of this layered approach in reducing the risk of unauthorized transactions and protecting users' financial information. The proposed framework aims to enhance trust in online financial platforms and ensure a safer digital transaction experience for users.

Index Terms—Multifactor authentication, machine learning, fraud detection, E-cyberboost