Abstract
Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.
Highlights
Internet of things (IoT), along with edge computing, has revolutionized industrial processes with the help of mobile devices such as tablets, smartphones, smartwatches, and PDAs
Experiments were conducted using the data set from VirusShare and Malgenome project. e proposed system attained a good accuracy of up to 98%. ough they studied many static features, the authors use dynamic features useful to detect zero-day and obfuscated malware
To analyse the impact of hybridization, we propose two machine learning-based hybrid malware analyzers, respectively, named HybriDroid and cHybriDroid. e HybriDroid framework exploits static as well as dynamic features for malware analysis using a hierarchical mechanism
Summary
Internet of things (IoT), along with edge computing, has revolutionized industrial processes with the help of mobile devices such as tablets, smartphones, smartwatches, and PDAs. We propose a comprehensive framework that incorporates both the static and dynamic analysis exploiting permissions and intents and considers important dynamic features such as data leakages, network connection manipulation, and enforcing special permissions. (1) a novel machine learning-based framework to analyse Android applications using a hierarchical approach (applying both the static and dynamic analysis) to detect known and zero-day malware,. (3) a dynamic analysis model that involves the investigation of system calls (such as network activity, files access, SMS activity, and call activity), external DexClass usage, cryptographic activity, run-time permissions enforcement, and rehashing to detect known and zero-day malware,. Ough the results of this research work produced a promising accuracy of up to 97%, more features like API calls and network statistics should be explored for a comprehensive dynamic analysis and a higher detection rate. In [11], the authors suggested an API sequence analysis-based dynamic
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