Updated the code

Author: Ap6pack

Pentesting/detectDDoS.py

@@ -10,15 +10,19 @@
 # Time limit for tracking IPs (e.g., 60 seconds)
 TIME_WINDOW = 60
 
+# Total packets processed
+total_packets = 0
+
 # Block an IP using iptables (for Linux systems)
 def block_ip(ip):
     try:
         # Block the IP using iptables
         subprocess.run(['iptables', '-A', 'INPUT', '-s', ip, '-j', 'DROP'], check=True)
-        print(f"Blocked IP: {ip}")
+        print(f"Blocked IP: {ip}")  # Log the blocked IP
     except subprocess.CalledProcessError as e:
-        print(f"Error blocking IP {ip}: {e}")
-
+    except subprocess.CalledProcessError as e:
+c
+c
 # Clean up old IPs from the dictionary based on a time window
 def clean_old_ips():
     current_time = time.time()
@@ -29,11 +33,16 @@ def clean_old_ips():
 
 # Function to detect a possible DDoS attack
 def detect_ddos(packet):
-    global ip_count
+    global ip_count, total_packets
+    total_packets += 1
+    print(f"Total packets processed: {total_packets}")  # Log total number of packets
+    
     if IP in packet:
         ip_src = packet[IP].src
         current_time = time.time()
 
+        print(f"Packet detected from: {ip_src}")  # Log each packet's source IP
+        
         # Clean up old IPs periodically
         clean_old_ips()
 
@@ -44,11 +53,14 @@ def detect_ddos(packet):
         else:
             ip_count[ip_src] = (current_time, 1)
 
+        # Log how many packets received from this IP
+        print(f"Packet count for {ip_src}: {ip_count[ip_src][1]}")  
+        
         # If packet count exceeds the threshold, block the IP
         if ip_count[ip_src][1] > PACKET_THRESHOLD:
             print(f"Possible DDoS attack from {ip_src}")
             block_ip(ip_src)
             del ip_count[ip_src]  # Stop tracking once blocked
 
 # Start sniffing network traffic and apply the detect_ddos function on captured IP packets
-sniff(filter="ip", prn=detect_ddos)
+sniff(filter="ip", prn=detect_ddos)

Stock-Diviend-Tracker/Stock-Dividend-Tracker.py

@@ -0,0 +1,70 @@
+import yfinance as yf
+import pandas as pd
+import schedule
+import time
+import matplotlib.pyplot as plt
+
+# Fetch dividend data for a single stock
+def fetch_dividend_data(ticker):
+    stock = yf.Ticker(ticker)
+    dividend_data = stock.dividends  # Fetch dividends history
+    return dividend_data
+
+# Calculate total expected dividend income for a single stock
+def calculate_dividend_income(ticker, shares_owned):
+    dividends = fetch_dividend_data(ticker)
+    if dividends.empty:
+        print(f"No dividend data available for {ticker}")
+        return 0
+    else:
+        # Sum up the total dividends paid in the last year
+        total_dividends_per_share = dividends.last('365D').sum()  # Dividends in the last 365 days
+        total_income = total_dividends_per_share * shares_owned
+        return total_income
+
+# Watchlist with the number of shares owned for each stock
+portfolio = {
+    "AAPL": 50,   # 50 shares of Apple
+    "MSFT": 30,   # 30 shares of Microsoft
+    "T": 100,     # 100 shares of AT&T
+    "JNJ": 25     # 25 shares of Johnson & Johnson
+}
+
+# Track expected dividend income for all stocks in the portfolio
+def track_dividend_income(portfolio):
+    dividend_summary = pd.DataFrame(columns=["Stock", "Shares Owned", "Expected Dividend Income"])
+
+    for stock, shares_owned in portfolio.items():
+        income = calculate_dividend_income(stock, shares_owned)
+        dividend_summary = pd.concat([dividend_summary, pd.DataFrame({"Stock": [stock], "Shares Owned": [shares_owned], "Expected Dividend Income": [income]})])
+
+    return dividend_summary
+
+# Create a dashboard to visualize expected dividend income for all stocks
+def create_dashboard(portfolio):
+    income_summary = track_dividend_income(portfolio)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    income_summary.plot(x="Stock", y="Expected Dividend Income", kind="bar", ax=ax)
+    plt.title("Expected Dividend Income from Portfolio")
+    plt.xlabel("Stock")
+    plt.ylabel("Expected Dividend Income (in USD)")
+    plt.show()
+
+# Job to calculate and save dividend income daily
+def job():
+    print("Updating expected dividend income data...")
+    summary = track_dividend_income(portfolio)
+    summary.to_csv('dividend_income_tracker.csv', index=False)
+    print(summary)
+
+# Schedule to run every day at 10 AM
+schedule.every().day.at("10:00").do(job)
+
+# Initial calculation and dashboard creation
+create_dashboard(portfolio)
+
+# Start the schedule loop after creating the dashboard
+while True:
+    schedule.run_pending()
+    time.sleep(1)

Stock-Diviend-Tracker/requirements.txt

@@ -0,0 +1,5 @@
+yfinance==0.2.43
+pandas==2.2.3
+numpy==2.1.1
+schedule==1.2.2
+matplotlib==3.9.2