Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm  

作　　者：V.Joseph Raymond R.Jeberson Retna Raj 

机构地区：[1]Department of Computer Science and Engineering,Sathyabama Institute of Science and Technology,Chennai,600119,Tamilnadu,India [2]School of Computing,SRM Institute of Science and Technology,Chennai,603203,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2023年第3期2147-2163,共17页计算机系统科学与工程（英文）

摘　　要：Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

关 键 词：Zero-day exploit hybrid analysis principal component analysis supervised learning smart cities 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP393.08[自动化与计算机技术—控制科学与工程]