Artificial Intelligence and Advanced Cybersecurity to Mitigate Credential-Stuffing Attacks in the Banking Industry
DOI:
https://doi.org/10.22399/ijcesen.754Keywords:
Credential-Stuffing, Machine learning, Stolen credentials, User behaviour analytics, Adaptive authentication, AIAbstract
Credential-stuffing attacks pose a critical threat to the banking sector, leveraging stolen login credentials to compromise user accounts and inflict substantial financial and reputational damage. Traditional security measures, including Multi-Factor Authentication (MFA) and CAPTCHA, often fall short against the sophistication of these attacks, necessitating more advanced and proactive defense strategies.
This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in cybersecurity, particularly in mitigating credential-stuffing threats. AI-driven solutions enable real-time threat detection, predictive analysis, and adaptive authentication, providing enhanced protection by analyzing large datasets to identify unusual login patterns and behaviors. Despite their promise, AI and ML adoption in cybersecurity faces challenges, including data privacy concerns, the risk of false positives and negatives, and scalability barriers. This research also examines emerging technologies, such as federated learning and blockchain-based authentication, which offer decentralized and privacy-preserving approaches to combating credential-stuffing attacks. Ultimately, AI and ML present the banking sector with powerful tools to build resilient, adaptable, and efficient defenses against evolving cyber threats. By integrating these technologies with complementary innovations, financial institutions can enhance security, protect customer trust, and address the dynamic landscape of credential-based cyberattacks.
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