A novel two phased approach combining deep learning and machinelearning classifiers for effective detection of turkish phishing web sites

With the increase in internet speed and the parallel rise in the number of internet-connected devices, online fraud has exhibited a significant surge in recent years. Attackers exploit platforms such as WhatsApp, email, SMS, mobile notifications, and social media messages to disseminate content that is attention-grabbing, intriguing, or fear-inducing. By inducing users to interact with these contents and click on embedded links, these malevolent actors redirect users to counterfeit websites that closely mimic authentic ones, thereby obtaining users' confidential information or engaging in various forms of deception. Commonly referred to as "phishing" sites, these malicious web pages are often used for such deceptive operations. Consequently, it is of paramount importance that mobile applications or browsers possess the capability to identify such harmful websites even before users access them. This study employs a two-stage approach to achieve a 98.4% success rate in identifying malicious sites. The dataset used consists of a list of malicious sites from the National Cyber Incident Response Center (USOM) alongside legitimate domain names. The dataset is divided into two subsets, namely Dataset1 and Dataset2. Dataset1 is employed to train a deep learning-based artificial intelligence model, which yields an accuracy rate of 92% upon completion of training. The websites within Dataset2 are subjected to the deep learning model in the initial stage to acquire phishing scores. Subsequently, by incorporating additional features pertaining to each website and employing a machine learning model for binary classification, the second stage of training facilitates the culmination of the ultimate outcome. Test results demonstrate the capacity to predict phishing incidents with a 98.4% accuracy score for a given website. Keywords: Online Fraud, Cyber Attack, Machine learning, Deep learning, Malicious URL