An evolutionary approach for achieving scalability with general regression neural networks

Casey, Kenan; Garrett, Aaron; Montgomery, Lacey S.; Dozier, Gerry

doi:10.1007/s11047-007-9052-x

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…For the standard AISs and mAISs, a sixfold crossvalidation was used where the training set consisted of 38 feature vectors associated with 20 benign and 18 malicious apps, the tuning set consisted of 10 feature vectors associated with 5 benign and 5 malicious apps and the test set also consisted of 10 feature vectors associated with 5 benign and 5 malicious apps similar to the setup used in [27]. Number of malicious apps AIS, mAIS The number of malicious apps in our test set.…”

Section: Resultsmentioning

confidence: 99%

An artificial immunity approach to malware detection in a mobile platform

Brown

Anwar

Dozier

2017

EURASIP J. on Info. Security

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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 detectors that match the information flows associated with benign apps. The mAIS presented in this paper 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. This approach achieved a 93.33% accuracy with a true positive rate of 86.67% and a false positive rate of 0.00%.

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Section: Resultsmentioning

confidence: 99%