An artificial immunity approach to malware detection in a mobile platform

@article{Brown2017AnAI,
  title={An artificial immunity approach to malware detection in a mobile platform},
  author={James Brown and Mohd Anwar and Gerry V. Dozier},
  journal={EURASIP Journal on Information Security},
  year={2017},
  volume={2017},
  pages={1-10}
}
Inspired by the human immune system, we explore the development of a new Multiple-Detector Set Artificial Immune System (mAIS) for the detection of mobile malware based on the information flows in Android apps. mAISs differ from conventional AISs in that multiple-detector sets are evolved concurrently via negative selection. Typically, the first detector set is composed of detectors that match information flows associated with malicious apps while the second detector set is composed of… CONTINUE READING
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Key Quantitative Results

  • Our results show the standard AIS has the ability to detect malicious apps with a true positive rate (TPR), correct classification rate, of 80.00%.

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A Survey on Android Malware Detection Techniques Using Machine Learning Algorithms.

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