Artificial immunity approach to malware detection in a mobile platform
The authors were inspired by the human immune system to 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.
Mobile devices are integrated in every aspect of society. The Android operating system encompasses a far-reaching range of users. With Android OS and the mobile industry in general, mobile malware has become a significant threat. Inspired by the human immune system, researchers in a recent study explored 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, published in EURASIP Journal on Information Security. Typically, the first detector set is composed of detectors that match information flows associated with malicious apps whereas the second detector set is composed of detectors that match the information flows associated with benign apps. The mAIS presented in this study incorporates feature selection along with a negative selection technique known as the split detector method (SDM). This new mAIS has been compared with a variety of conventional AISs and mAISs using a dataset of information flows captured from malicious and benign Android applications.