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Android Malware Detection Method Based on Permission Complement and API calls

Chinese Journal of Electronics 2022年第4期31卷 773-785页

作者： YANG Jiyun TANG Jiang YAN Ran XIANG Tao College of Computer Science Chongqing University

The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models,i.e. Model I, Model II, and Model III are defined. For the Model I type malware, its malicious behavior occurs in Dex Code, so the application programming interface(api)classes were used to characterize the behavior of the DexCode file. For the Model II type and Model III type malwares whose malicious behaviors occur in an external file,the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.

关键词： Android malicious application detection method threat models API calls Data security malicious behavior Android malware detection method Operating systems DexCode file executable files application programming interface classes Android (operating system) Ubiquitous and pervasive computing permission complement invasive software external file dynamic code loading mechanism application program interfaces feature selection method feature selection

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Swarm Optimization and Machine Learning for Android Malware Detection

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Computers, Materials & Continua 2022年第12期73卷 6327-6345页

作者： K.Santosh Jhansi P.Ravi Kiran Varma Sujata Chakravarty Centurion University of Technology and Management ParalakhemundiOdishaIndia Maharaj Vijayaram Gajapathi Raj College of Engineering VizianagaramIndia Centurion University of Technology and Management BhubaneswarOdishaIndia

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

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