How Artificial Intelligence Can Protect Financial Institutions From Malware Attacks
Abstract
The objective of this study is to examine the potential of artificial intelligence (AI) to enhance the security posture of financial institutions against malware attacks. The study identifies the current trends of malware attacks in the banking sector, assesses the various forms of malware and their impact on financial institutions, and analyzes the relevant security features of AI. The findings suggest that financial institutions must implement robust cybersecurity measures to protect against various forms of malware attacks, including ransomware attacks, phishing attacks, mobile malware attacks, APTs, and insider threats. The study recommends that financial institutions invest in AI-based security systems to improve security features and automate security tasks. To ensure the reliability and security of AI systems, it is essential to incorporate relevant security features such as explain ability, privacy, anomaly detection, intrusion detection, and data validation. The study highlights the importance of incorporating explainable AI (XAI) to enable users to understand the reasoning behind the AI's decisions and actions, identify potential security threats and vulnerabilities in the AI system, and ensure that the system operates ethically and transparently. The study also recommends incorporating privacy-enhancing technologies (PETs) into AI systems to protect user data from unauthorized access and use. Finally, the study recommends incorporating robust security measures such as anomaly detection and intrusion detection to protect against adversarial attacks and data validation and integrity checks to protect against data poisoning attacks. Overall, this study provides insights for decision-makers in implementing effective cybersecurity strategies to protect financial institutions from malware attacks.
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