A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

Currently, the smartphone contains lots of sensitive information. The increasing number of smartphone usage makes it more interesting for phishers. Existing phishing detection techniques are performed on their specific features with selected classifiers to get their best accuracy. An effective phishing detection approach is required to adapt the concept drift of mobile phishing and prevent degradation in accuracy. In this work, an adaptive phishing detection approach based on case-based reasoning technique is proposed to handle the concept drift challenge in phishing apps. Several experiments are conducted in order to demonstrate the design decision of our proposed model. The proposed model is evaluated with a large feature set containing 1,065 features from 10 different categories. These features are extracted from more than 10,000 android applications. Five combinations of features are created in order to mimic new real-world Android apps to evaluate our experiments. Moreover, a reduced feature set is also studied in this work in order to improve the efficiency of the proposed model. Both accuracy and efficiency of the proposed model are evaluated. The experimental results show that our proposed model achieves acceptable accuracy and efficiency for the phishing detection.