MACHINE LEARNING ALGORITHMS FOR CYBER ATTACKS AND FRAUD DETECTION
Rahul Choudhary, Rajkumar Choudhary, Karuna Soni · International Journal of Technical Research & Science · 2024
Cyber-attacks and fraud pose significant risks to individuals, organizations, and nations. The consequences of such malicious activities range from financial losses and reputational damage to national security threats. As cyber attackers continuously evolve their techniques, traditional defense mechanisms often fall short in providing adequate protection. Consequently, there is a pressing need for advanced, adaptive, and efficient solutions to detect and mitigate these threats. The rise of cyber-attacks and fraud has become a significant concern for both individuals and organizations. As a result, the need for advanced machine learning algorithms to detect and prevent these threats has never been greater.