From 7986909f3c5a78a4f9bc1c00faa1c59b6f203352 Mon Sep 17 00:00:00 2001
From: Roy SALIBA <salibaroy3@gmail.com>
Date: Wed, 29 May 2024 22:49:33 +0200
Subject: [PATCH] Create Iamgrok

---
 jesappellegrok | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 jesappellegrok

diff --git a/jesappellegrok b/jesappellegrok
new file mode 100644
index 0000000..cda5820
--- /dev/null
+++ b/jesappellegrok
@@ -0,0 +1,72 @@
+import re
+import numpy as np
+from datetime import datetime
+
+# Fonction pour scanner plusieurs entrées
+def scan_entries(entries):
+    results = []
+    for entry in entries:
+        scan_result = scan_entry(entry)
+        results.append(scan_result)
+    return results
+
+# Fonction pour scanner une seule entrée
+def scan_entry(entry):
+    scam_patterns = [
+        re.compile(r'scam_offer'),
+        re.compile(r'login\.php\?username=admin&password=admin'),
+        re.compile(r'transfer\.php')
+    ]
+    scam_activities = detect_scams(entry, scam_patterns)
+    return scam_activities
+
+# Fonction pour détecter des scams dans les logs
+def detect_scams(log_data, patterns):
+    lines = log_data.split('\n')
+    scam_lines = []
+    for line in lines:
+        for pattern in patterns:
+            if pattern.search(line):
+                scam_lines.append(line)
+                break
+    return scam_lines
+
+# Fonction pour calculer les dérivées
+def calculate_derivatives(data):
+    data = np.array(data)
+    derivatives = np.diff(data)
+    return derivatives
+
+# Exemple d'utilisation
+log_entries = [
+    """
+    192.168.1.1 - - [28/May/2024:10:32:55 +0000] "GET /index.html HTTP/1.1" 200 2326
+    192.168.1.2 - - [28/May/2024:10:33:12 +0000] "GET /login.php?username=admin&password=admin HTTP/1.1" 200 1420
+    192.168.1.3 - - [28/May/2024:10:34:23 +0000] "POST /transfer.php HTTP/1.1" 200 5320
+    192.168.1.4 - - [28/May/2024:10:35:00 +0000] "GET /scam_offer HTTP/1.1" 200 221
+    """,
+    # Ajoutez d'autres entrées ici
+]
+
+scanned_results = scan_entries(log_entries)
+
+# Supposons que chaque scan_result contient le nombre d'activités suspectes détectées
+activity_counts = [len(result) for result in scanned_results]
+
+# Calculer les dérivées des activités suspectes détectées
+activity_derivatives = calculate_derivatives(activity_counts)
+
+# Afficher les résultats
+print("Scanned Results:", scanned_results)
+print("Activity Counts:", activity_counts)
+print("Activity Derivatives:", activity_derivatives)
+
+# Sauvegarder les résultats dans un fichier
+timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+output_filename = f"scan_results_{timestamp}.txt"
+with open(output_filename, 'w', encoding='utf-8') as file:
+    file.write(f"Scanned Results: {scanned_results}\n")
+    file.write(f"Activity Counts: {activity_counts}\n")
+    file.write(f"Activity Derivatives: {activity_derivatives}\n")
+
+print(f"Results saved to {output_filename}")