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An Android Machine Learning Method for the Identification of Malicious Applications

Nikita .

Abstract


In this paper, an AI approach for discovery of vindictive applications in an Android working framework is proposed. In this day and age, android is a biggest working framework utilized by people groups all over the planet and its clients are as yet developing step by step. As android is utilized for an enormous scope by its clients and because of its fame, the vindictive applications are likewise developing to hurt the framework or to utilize the individual data of an android client. A new report by Google said that the organization distinguished the vast majority of applications with vindictive substance before anybody could introduce them. The execution of vindictive application discovery apparatus dissect the connection between framework capabilities, touchy consents and delicate application programming points of interaction. The different AI procedure, Network Search and SVM Calculation is performed to dissect the outcome that can distinguish the pace of malevolent applications with an improved proficiency and can work on the exhibition of the framework with the AI technique.


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References


L. Wei et al.:Machine Learning-Based Malicious Application Detection of Android

Zhenlong Yuan et al.: Droid Detector: Android Malware Characterization and Detection Using Deep Learning

S. Arshad et al. SAMADroid: Novel 3-Level Hybrid Malware Detection Model for Android Operating System

P. Feng et al. Novel Dynamic Android Malware Detection System With Ensemble Learning

M F. A. Narudin et al. Evaluation of machine learning classifiers for mobile malware detection:

Aafer, Y., Du, W., & Yin, H. (2013). DroidAPIMiner: Mining API-level features for robust malware detection in android. In Security and Privacy in Communication Networks (pp. 86-103). Springer International Publishing.

The Concept of Attack Scenarios and its Applications in Android Malware Detection: IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems

Using Security Attack Scenarios To Analyse Security During Information Systems Design

Androguard,[Online].Available: https://code.google.com/p/androguard/.

Parkour, M. (2013). Contagiodump [Online]. Available: http://contagiominidump.blogspot.tw/.


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