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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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