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Recommender system for predicting malicious Android applications

Recommender system for predicting malicious Android applications

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Hackers spread malware for various reasons, yet regularly the thought processes are money related. Malignant Android battles intended to take charge card and keeping money-related data from tainted gadgets were most common, regularly notwithstanding utilizing the official Google Play Store to trap casualties into entering their Master card data. Guiltless client does not know about the way that the application which he will download is sheltered or pernicious. The key thought is to manufacture a central server that will accumulate the clients'applications information and play out the static and dynamic investigation of an Android application to locate the risky examples and will group it as malicious or benign. This assignment will require huge processing power. Here, the importance of big data comes into picture. Already existed and suggested frameworks have been tremendously helpful, and huge information is the main impetus behind proposal frameworks. Our planned mechanism additionally plans to gather a lot of client information; for example, it adds up to a number of downloads, clients'audits, consents required by an application, and designers data to give relevant and powerful proposals. There is a need of a dynamic malware investigation system which uses the innovations of graphical user interface (GUI)-based testing, big data examination, and machine figuring out how to identify malignant Android applications. The system can be utilized as a part of conjunction with other existing attempts to enhance the discovery rate of malware.

Chapter Contents:

  • 10.1 Background
  • 10.1.1 Android operating system architecture
  • 10.1.1.1 Applications
  • 10.1.1.2 Application framework
  • 10.1.1.3 Android runtime
  • 10.1.1.4 Libraries
  • 10.1.1.5 Kernel
  • 10.1.2 Android application structure
  • 10.1.3 Application threats
  • 10.2 The proposed recommender system for mobile application risk reduction
  • 10.2.1 Preprocessing
  • 10.2.2 Emulation and testing
  • 10.2.3 Features extraction
  • 10.2.4 Machine learning
  • 10.2.5 Dataset
  • 10.3 Conclusion
  • References

Inspec keywords: graphical user interfaces; invasive software; recommender systems; mobile computing; Android (operating system)

Other keywords: malignant Android applications; malicious Android applications; money-related data; official Google Play Store; Big Data examination; client information; guiltless client; charge card; tainted gadgets; planned mechanism; dynamic investigation; dynamic malware investigation system; recommender system; malignant Android; Master card data; static investigation

Subjects: Information networks; Search engines; Data security; Ubiquitous and pervasive computing; Graphical user interfaces; Operating systems

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