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SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

Ahmad Karim1, Rosli Salleh1, Muhammad Khurram Khan2

  • 1Department of Computer Systems and Technology, University of Malaya, Kuala Lumpur, Malaysia.

Plos One
|March 16, 2016
PubMed
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Mobile botnets pose a growing threat, but SMARTbot effectively detects malicious applications. This novel framework uses machine learning to identify botnet binaries with high accuracy, even with obfuscated code.

Area of Science:

  • Cybersecurity
  • Mobile Security
  • Machine Learning Applications

Background:

  • The proliferation of smartphones has led to an evolution of botnet threats, mirroring the impact seen on personal computers.
  • Mobile botnets, similar to traditional PC botnets, are networks of compromised devices remotely controlled by cybercriminals for malicious activities.
  • The increasing sophistication of mobile threats necessitates proactive security measures and detection methods.

Purpose of the Study:

  • To propose and evaluate SMARTbot, a novel dynamic analysis framework for the automatic detection of mobile botnet binaries.
  • To leverage machine learning techniques for enhanced accuracy in identifying malicious mobile applications.
  • To develop a benchmark dataset for future research in mobile botnet detection.

Main Methods:

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  • SMARTbot employs an off-device behavioral analysis framework.
  • The framework utilizes Artificial Neural Networks' back-propagation method to generate a mobile botnet learning model.
  • Machine learning classifiers, including logistic regression, were used to detect botnet binaries from a malicious corpus.

Main Results:

  • SMARTbot demonstrated remarkable accuracy in detecting mobile botnet binaries, even those with obfuscated code.
  • A classifier model based on simple logistic regression achieved 99.49% accuracy in botnet app detection.
  • Manual inspection of the botnet dataset revealed significant trends in malicious application behavior.

Conclusions:

  • SMARTbot provides an effective and accurate solution for detecting mobile botnet binaries.
  • Logistic regression proved to be a highly effective machine learning algorithm for this detection task.
  • The newly devised mobile botnet dataset will serve as a valuable resource for future cybersecurity research.