Detection System of Whaling Attack using Deep Learning Techniques

Cyber security attacks are becoming among the most common issue that deals with governments, general internet and organizations. “Whaling” is a kind of targeted phishing that aims specifically at senior executives by masquerading as legitimate email. A kind of social engineering-based digital fraud known as “whaling” involves tricking victims into doing a secondary action, such as starting a wire transfer of money. As a defence against this danger, this study suggested a deep learning-based method for detecting whaling attacks. The dataset is initially partitioned for training the detection model and evaluating its results using the test data in order to capture the inherent qualities of the email text. In order to train and evaluate the whaling email detection accuracy using the collected features, a neural network technique was selected. The additional features are categorized into three groups such as whaling email group, group of approved emails and junk emails. Experimental analysis is analysed by comparing existing methods with the proposed methods. After making evaluation between them, the proposed method achieved the most accurate and efficient results.