November 2019
Intermediate to advanced
346 pages
9h 36m
English
In this recipe, we detail how to train MalConv on raw PE files:
import numpy as npfrom tqdm import tqdm
def embed_bytes(byte): binary_string = "{0:08b}".format(byte) vec = np.zeros(8) for i in range(8): if binary_string[i] == "1": vec[i] = float(1) / 16 else: vec[i] = -float(1) / 16 return vec
import osfrom os import listdirdirectories_with_labels = [("Benign PE Samples", 0), ("Malicious PE Samples", 1)]list_of_samples = []labels = []for dataset_path, label in directories_with_labels: samples = [f for f in ...