Enhancing Case-based Reasoning Approach using Incremental Learning Model for Automatic Adaptation of Classifiers in Mobile Phishing Detection

Nowadays, millions of mobile phone users over the world are put at risk by phishing while more than 3.8 billion smartphones are estimated to be used in 2020 [1]. As a consequence, the security of these devices becomes a top priority. Moreover, mobile devices become the primary means of communication and information access [2]. Thus, in our prior work [3], some analyses on the literatures of phishing detection are performed and identified the important features for the mobile phishing detection. Then, the adaptive mobile phishing detection model is proposed in another prior work [4] by using a Case-based Reasoning (CBR) approach. In our previous work [4], the experiments were conducted to demonstrate the design decision of our proposed model and to verify the performance in handling the concept drift. However, the main challenge faced by the CBR approach is learning a new case in order to adapt the system to a new phishing pattern. The mismatching input features with the existing cases in the case-base was lacking in our prior work [4]. In this work, the incremental learning model for the adaptation to the new examples to the case-base is proposed.