diff --git a/Time Series Visualization of CSV Data Extracted from PCAP Files/Kernel Density Estimation (KDE)/Kernel Density Estimation.py b/Time Series Visualization of CSV Data Extracted from PCAP Files/Kernel Density Estimation (KDE)/Kernel Density Estimation.py
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+++ b/Time Series Visualization of CSV Data Extracted from PCAP Files/Kernel Density Estimation (KDE)/Kernel Density Estimation.py	
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+import numpy as np
+import time, os, sys
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+from scipy.stats import norm, gaussian_kde
+from statsmodels.tsa.ar_model import AutoReg
+from sklearn.mixture import GaussianMixture
+
+#Kernel Density Estimation (KDE)
+def plot_kde_seaborn(df, filename, column, output_dir):
+    data = df
+    # Extract relevant columns
+    ingoing_column_name = f'{column}_ingoing'
+    outgoing_column_name = f'{column}_outgoing'
+    
+    ingoing = data[ingoing_column_name]
+    outgoing = data[outgoing_column_name]
+    
+    plt.figure(figsize=(12, 6))
+    sns.kdeplot(ingoing, label=f'{column}_ingoing', fill=True)
+    sns.kdeplot(outgoing, label=f'{column}_outgoing', fill=True)
+    plt.xlabel(f'{column}_ingoing|outgoing')
+    plt.title("Kernel Density Estimation (KDE)")
+    plt.legend()    
+    plot_filename = f'{filename}_{column}_kde_sns.svg'
+    
+    plt.savefig(os.path.join(output_dir, plot_filename))
+    plt.show()
+    plt.close()
+
+def main(in_csv, out_dir):
+    columns_bypass = ['timestamp', 'time_diff', 'source_ip', 'destination_ip']
+    for filename in os.listdir(in_dir):
+        if filename.endswith(".csv"):
+            filename_without_ext, ext = os.path.splitext(filename)
+            print(f"CSV File:\t{filename_without_ext}")
+            packet_data = []
+            csv_file_path = os.path.join(in_dir, filename)
+            df = pd.read_csv(csv_file_path)
+            data = df
+            data['timestamp'] = pd.to_datetime(data['timestamp'])
+            for column in data.columns:
+                if (data[column] == 0).all():
+                    columns_bypass.append(column)
+            # Iterate over each column (excluding 'timestamp' and 'time_diff') and plot KDE
+            for column in data.columns:
+                if column not in columns_bypass:
+                    try:
+                        parts = column.split("_", 2)
+                        protocol_name = "_".join(parts[:2])
+                        plot_kde_seaborn(data, filename_without_ext, protocol_name, out_dir)
+                    except ValueError as e:
+                        print(f"Could not plot KDE for column {column}: {e}")
+
+if __name__ == "__main__":
+  print("[" + __file__ + "]'s last modified: %s" % time.ctime(os.path.getmtime(__file__)))
+  # Check if a parameter is provided
+  if len(sys.argv) == 4 :
+    in_dir = sys.argv[1]
+    if not os.path.exists(in_dir):
+        print(f"Directory: '{in_dir}' does not exist.")
+        exit()
+    print(f"\nCSV Directory:\t\t{in_dir}")
+
+    out_dir = sys.argv[2]
+    if not os.path.exists(out_dir):
+        os.makedirs(out_dir, exist_ok=True)
+    print(f"SVG Files will save:\t{out_dir}")
+    
+    IS_MALWARE = sys.argv[3]
+    print(f"DATASET is malware:\t{IS_MALWARE}\n\n")
+    main(in_dir, out_dir)
+  else:
+    print("No input directory and output directory provided.")
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