Abstract

Whaling attacks is a sophisticated spear phishing form of targeting high-level executives. It poses a significant threat to the organization's cybersecurity. This research presents and introduces a novel intelligent system, innovative in detecting and preventing all the complex threats of enterprise environments. The power of artificial intelligence with Python implements a system that uses Natural Language Processing (NLP) to analyze the contents of emails for identifying the subtle indicators of malicious intent through suspicious patterns, sentiment analysis, and contextual cues. The performance of the proposed system was high because a hybrid model of machine learning by using a Support Vector Machine (SVM) classifier, with integrating TF-IDF vectorization, was used to enhance the accuracy of detecting malicious email data. The comprehensive evaluation in a real-world dataset for whaling attacks demonstrates the efficacy of an AI-driven approach by achieving exceptional results with a precision of 99.2%, a recall of 98%, and an F1-score of 98.596%. Furthermore, the proposed intelligence system is novel because it incorporates real-time threat detection to prevent effective capabilities in whaling attacks by minimizing false positives and accelerating response times. This system will deliver an essential contribution to the cybersecurity practices, offering an adaptive solution that fortifies enterprise defenses against increasingly sophisticated social engineering attacks, and for reducing the financial fraud risk and data breaches.