Machine Learning Algorithms for Distributed Denial of Service (DDoS) Detection in the Banking Sector Using IoT-Based Monitoring Techniques

Large-scale cyberattacks are becoming more and more likely to target banks. Because banks are interconnected, a cyberattack on one might put the solvency of a financial establishment at risk. Cybercrime has increased since more people use mobile banking and the Internet. Fraudulent activities such as identity theft, ATM robberies, and credit card scams are examples of cybercrime. The substantial financial the data's value held by the banking industry makes it particularly vulnerable. The potential attack surface has increased with the growth of banks' digital footprints. Cyberattacks have the potential to result in confidential information leaks, power disruptions, and malfunctioning military equipment. They might lead to the theft of priceless private information. They can paralyze systems or interfere with computer and phone networks, making data unavailable. The banking sector is especially vulnerable because of the substantial financial value of the information it contains. Hackers can make money in various ways using the financial data and banking credentials they have taken. A distributed denial-of-service attack (DDoS) is a type of online fraud that can affect the speed at which websites load, especially those run by other financial organizations and banks. DDoS attacks happen when many systems overwhelm a targeted system's resources or bandwidth.