Swarm Optimization and Machine Learning for Android Malware Detection  

作　　者：K.Santosh Jhansi P.Ravi Kiran Varma Sujata Chakravarty 

机构地区：[1]Centurion University of Technology and Management,Paralakhemundi,Odisha,India [2]Maharaj Vijayaram Gajapathi Raj College of Engineering,Vizianagaram,India [3]Centurion University of Technology and Management,Bhubaneswar,Odisha,India

出　　处：《Computers, Materials & Continua》2022年第12期6327-6345,共19页计算机、材料和连续体（英文）

摘　　要：Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.

关 键 词：Android malware API calls auto-encoders ant lion optimization cuckoo search optimization firefly optimization artificial neural networks artificial neuronal classifier 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] TP181[自动化与计算机技术—计算机科学与技术]