Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection

Figure 2

Skip-gram learning process for target word and-int and context words div-long, cmpg-double, and return-void. Input is one hot vector of target word while output tries to predict correct context words. Dense vector for each word is weighted from corresponding input neuron to embedded layer neurons.