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| """Python 3 script for analyzing the dataset labels and for creating
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| tables and images.
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| Before running the script, install the Python 3 dependencies by
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| executing the command `pip3 install requirements.txt`.
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| """
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| import matplotlib.pyplot as plt
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| import numpy as np
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| import pandas
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| import seaborn as sns
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| from tabulate import tabulate
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| LABELS_FOLDER = "../../labels/"
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| TABLES_FOLDER = "../tables/"
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| IMAGES_FOLDER = "../images/"
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| BENIGN_LABELS = LABELS_FOLDER + "benign.csv"
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| MALWARE_LABELS = LABELS_FOLDER + "malware.csv"
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| LABELS_TABLE = TABLES_FOLDER + "labels.md"
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| UNIVARIATE_ANALYSIS_TABLE = TABLES_FOLDER + "univariate_analysis.md"
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| DISTRIBUTION_IMAGE = IMAGES_FOLDER + "distribution.png"
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| HISTOGRAMS_IMAGE = IMAGES_FOLDER + "histograms.png"
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| benign_df = pandas.read_csv(BENIGN_LABELS)
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| malware_df = pandas.read_csv(MALWARE_LABELS)
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| all_df = pandas.merge(benign_df, malware_df, how="outer")
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| all_df["is_malware"] = all_df.malice > 0
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| all_df = all_df.groupby(["is_malware", "type"]).size().unstack(level=0)
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| sns.heatmap(data=all_df,
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| xticklabels=["Benign", "Malicious"],
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| yticklabels=["PE", "OLE"],
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| annot=True,
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| fmt="d")
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| plt.xlabel("Malice")
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| plt.ylabel("Filetype")
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| print("[+] The plot with the samples distribution was saved.")
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| plt.savefig(DISTRIBUTION_IMAGE)
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| column_types = [(column_type[0], str(column_type[1]))
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| for column_type in malware_df.dtypes.items()]
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| labels_table = tabulate(column_types, ["Name", "Type"], tablefmt="github")
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| with open(LABELS_TABLE, "w") as labels_file:
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| labels_file.write(labels_table)
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| print(
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| "[+] The table containing the characteristics of the dataset was dumped.")
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| analysis_malware_df = malware_df.loc[:, malware_df.columns != "type"]
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| analysis_df = analysis_malware_df.describe(percentiles=[])
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| analysis_df = analysis_df.drop(["count", "50%"])
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| uni_analysis_table = tabulate(analysis_df, headers="keys", tablefmt="github")
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| with open(UNIVARIATE_ANALYSIS_TABLE, "w") as uni_analysis_file:
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| uni_analysis_file.write(uni_analysis_table)
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| print("[+] The table containing the univariate analysis was dumped.")
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| numeric_columns = analysis_malware_df.select_dtypes(
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| include=np.number).columns.tolist()
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| plots_count = len(numeric_columns)
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| total_cols = 2
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| total_rows = plots_count // total_cols
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| fig, axs = plt.subplots(nrows=total_rows,
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| ncols=total_cols,
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| figsize=(7 * total_cols, 7 * total_rows),
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| constrained_layout=True)
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| for i, column_name in enumerate(numeric_columns):
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| row = i // total_cols
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| column = i % total_cols
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| plot = sns.histplot(data=analysis_malware_df,
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| x=column_name,
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| ax=axs[row][column])
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| if (i == 0):
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| new_label = column_name.capitalize()
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| else:
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| new_label = column_name.capitalize() + " Family Membership"
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| plt.setp(axs[row, column], xlabel=new_label)
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| plt.setp(axs[:, :], ylabel="Count")
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| plt.savefig(HISTOGRAMS_IMAGE)
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| print("[+] The plot containing a histogram for each numeric label was saved.")
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