The advent of the digital era has brought unprecedented convenience and efficiency to the banking sector. However, it has also exposed financial institutions to a multitude of cyber threats. In particular, the increasing value of banking information in the digital realm has made it an attractive target for malicious actors. The repercussions of successful hacking attempts on these systems can be severe, ranging from financial losses to compromised customer data and erosion of trust in the banking sector. Consequently, bolstering the security of banking systems has become a paramount concern. This paper undertakes a comprehensive analysis of the security landscape surrounding financial organizations by leveraging a bank dataset comprising 15,000 samples and 38 variables. Through rigorous data analysis techniques, the dataset is utilized to train a Random Forest algorithm, which is then employed to evaluate and identify Distributed Denial-of-Service (DDoS) attacks launched against financial institutions. The results of this study are highly promising, as the Random Forest algorithm achieves an impressive accuracy rate of 99% in identifying potential security flaws. By providing valuable insights and empirical evidence, this research contributes to the existing body of knowledge in the field of cyber security, specifically concerning the detection and prevention of DDoS attacks in financial organizations.