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Quorum Chain-Based Malware Detection in Android Smart Devices

  • Fei Gao
  • Frank JiangEmail author
  • Yuping Zhang
  • Robin Doss
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1113)

Abstract

Smart devices are gradually becoming indispensable in people’s daily lives, and Android-based smart devices are taking over the main stream in mobile devices. However, while Android smart devices bring convenience to customers, they also bring problems. Due to the open-sourced nature of the Android system, malicious programs and software attacks pose a significant security risk to user data. Therefore, the detection of malware has always been a critical issue. For a long time, various malware detection schemes have been proposed, which have gradually improved the detection of malware. Traditional detection methods are based on static or dynamic detection techniques. In recent years, with the advancement of technology, malware detection based on machine learning ideas has been widely used, such as K-NN, deep learning, decision trees, and so on. Blockchain has been widely used in many fields since its birth. This paper combines traditional detection methods and ensemble learning algorithms to propose a malware detection technology based on QuorumChain framework (blockchain technology). The experimental results verify that the proposed new model is better than other models in precision, recall and f1-measure.

Keywords

Android devices Malware detection Quorum chain 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fei Gao
    • 1
  • Frank Jiang
    • 2
    Email author
  • Yuping Zhang
    • 3
  • Robin Doss
    • 2
  1. 1.College of Electronics EngineeringGuangxi Normal UniversityGuilinChina
  2. 2.School of Info TechnologyDeakin UniversityGeelongAustralia
  3. 3.Chengdu Technological UniversityChengduChina

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