A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

作     者：Attia Qammar Ahmad Karim Yasser Alharbi Mohammad Alsaffar Abdullah Alharbi 

作者机构：Department of Information TechnologyBahauddin Zakariya UniversityMultan60000Pakistan College of Computer Science and EngineeringUniversity of HailHa’il81451Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第43卷第12期

页      面：915-930页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Android botnet botnet detection hybrid analysis machine learning classifiers mobile malware 

摘      要：Smartphone devices particularly Android devices are in use by billions of people everywhere in the ***,this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control(C&C)method to expand malicious *** present,mobile botnet attacks launched the Distributed denial of services(DDoS)that causes to steal of sensitive data,remote access,and spam generation,***quently,various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic *** this paper,a novel hybrid model,the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is ***,results are evaluated using machine learning *** Random Forest(RF)classifier outperform as compared to other ML techniques i.e.,Naïve Bayes(NB),Support Vector Machine(SVM),and Simple Logistic(SL).Our proposed framework achieved 97.48%accuracy in the detection of botnet ***,some future research directions are highlighted regarding botnet attacks detection for the entire community.