Phishing, commonly known as the unauthorized acquisition of personal information from users of online platforms and clients of digital stores and financial institutions, has witnessed a notable surge in recent years. This surge has fueled the growth of a thriving criminal enterprise, particularly targeting financial service providers. Given the magnitude of this threat, we adopted a dual approach involving the application of a Principal Component Analysis (PCA) algorithm and a multi-layer perceptron neural network algorithm to identify and combat phishing attacks within the realm of electronic banking. Initially, we employed the PCA algorithm to streamline the identification process, reducing the number of features from an initial 30 to a more manageable 14. Following this feature reduction step, we fine-tuned the accuracy of detecting phishing website attacks using the multi-layer perceptron neural network algorithm. This algorithm, functioning as a binary classification technique, adeptly determines whether an input vector belongs to a specific class. Acting as a linear classifier, it relies on the weighted linear combination of input factors to make predictions. To further fortify our defenses, we implemented the Waka tool, an online algorithm capable of meticulously examining individual inputs. Through the strategic integration of the PCA and multi-layer perceptron neural network algorithms, we achieved a substantial enhancement in the accuracy of detecting phishing website attacks in the electronic banking domain, reaching an impressive 91.64%.