Android malware has become a potential risk to mobile applications. Due to the increase in the usage of Android mobile applications in the world, the attackers target it to spread malware. Malware is the malicious software that can cause damage to a system of mobile device in terms of removing data or denying a service and so on. Malware can be one of the forms of cyber security threats. It has history of damaging potential applications in the real world. The cyber security threat landscape is increased drastically with the presence of Android malware. The rationale behind this is that people of all walks of life started using Android smart phones for virtually any operation including banking and shopping.
As the mobile smart phones and associated sensors produce huge amount of data known as big data, it became crucial to protect Android applications from malware. Big data has become an important buzzword and there are enterprises that depend on the business intelligence acquired from big data for decision making. In this context, it is important to have an efficient mechanism to detect and prevent Android malware. Many approaches came into exultance as found in the literature. They include signature based approaches 1, commercial malware detection methods 2, detection methods that also support Dalvik byte code transformations 4, data flow path based solutions 5 and 6, characterization of malware through system calls 9 and a hybrid approach that combines both API-calls and permission based approaches. In 12 an iterative approach is followed with multiple classifiers to detect malware. From the literature it is found that the methods are focusing more on accuracy of the solution rather than the computational complexity. In this paper we proposed an approach that focuses more on reducing computational complexity and increasing accuracy of the detection method. Our contributions are as follows.
• We proposed a framework to have a systematic approach in Android malware detection. It is based on the significant permission based permission reduction, pruning and ranking approaches.
• We proposed an algorithm known as Permission Significance-based Pruning for Android Malware Detection (PSP-AMD) to build a classifier that provides accuracy of detection and reduces computational complexity.
• We built a prototype application that demonstrates proof of the concept. The empirical results revealed the utility of the proposed solution which is light weight and focuses not only on the accuracy but also reduction of computational complexity.
The remainder of the paper is structured as follows. Section 2 provides review of literature. Section 3 presents the proposed system in detail. Section 4 presents experimental results while section 5 concludes the paper and provides future scope.