FinTech Mobile APP Intelligent Safety Environment Research
Project Overview
This research project is divided into three phases.
In the first year, a fast and effective system will be developed based on machine learning models to detect malicious Android apps.
In the second year, the knowledge representation characteristics of the system will be used to effectively outline the unchangeable behavior features of mobile malware. Associative PetriNet (APN) and Apriori algorithm will be used as the basis to effectively reduce virus detection rules, find the most concise and valuable set of detection logic rules, reduce the system performance and resource during detection, and achieve the goal of high-performance detection.
In the third year, the project will adopt Deep Learning to construct a “Deep Learning Malware Detection and Analysis Model”. The excellent feature extraction capability of Convolutional Neural Network (CNN) will be used to conduct real-time analysis and prevention of malicious behavior.
Source: Wikimedia Commons
