Systems and methods for malware detection using multiple neural networks are provided. According to one embodiment, for each training sample, a supervised learning process is performed, including: (i) generating multiple code blocks of assembly language instructions by disassembling machine language instructions contained within the training sample; (ii) extracting dynamic features corresponding to each of the code blocks by executing each of the code blocks within a virtual environment; (iii) feeding each code block into a first neural network and the corresponding dynamic features into a second neural network; (iv) updating weights and biases of the neural networks based on whether the training sample was malware or benign; and (v) after processing a predetermined or configurable number of the training samples, the neural networks criticize each other and unify their respective weights and biases by exchanging their respective weights and biases and adjusting their respective weights and biases accordingly.