Malware Detection in Android Applications Using Integrated Static Features
作者： A. S. Ajeena Beegom , Gayatri Ashok
DOI: 10.1007/978-981-15-4825-3_1
关键词: Upload 、 The Internet 、 Opcode 、 Classifier (UML) 、 Android (operating system) 、 Artificial intelligence 、 Machine learning 、 Usability 、 Support vector machine 、 Computer science 、 Malware
摘要: Android operating systems based mobile phones are common in nowadays due to its ease of use and openness. Hundreds applications uploaded the internet every day, which can be benign or malicious. The increase growth malicious is alarming. Hence advanced solutions for detection malware needed. In this paper, a novel framework proposed that uses integrated static features Support Vector Machine (SVM) classifier. considered include permissions, API calls opcodes. Out these features, most significant ones selected using Pearson correlation coefficient N-grams. Each then fed experimental evaluation method comparison with existing methods shows better.
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