An Immunity Based Computer Virus Detection Method with GA-RVNS

Qin, Renchao; Li, Tao

doi:10.1109/iita.2008.258

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done to simplify the code, which allows the radius of each detector to be defined once instead for each dimension. As suggested in Qin et al ( 2008 ), the generation of detectors is focused on minimum number of detector yet covering all nonself space to identify and detect nonself classes. The mechanism used for detector generation takes into account the optimal detector center with maximum detector radius to influence the region having a constraint of not to detect self-matching samples.…”

Section: Methodsmentioning

confidence: 99%

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Rashid

Iqbal

Mahmood

et al. 2018

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV–Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.

show abstract

Section: Methodsmentioning

confidence: 99%