Create cyber security in python

cyber security in python

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+import scapy.all as scapy
+import paramiko
+import nmap
+import requests
+from bs4 import BeautifulSoup
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+import tensorflow as tf
+
+
+def scan_ports(target):
+    nm = nmap.PortScanner()
+    nm.scan(target, '1-1024')  # Scanning ports 1 to 1024
+    return nm.all_hosts()
+
+
+def sniff_packets(interface):
+    scapy.sniff(iface=interface, store=False, prn=process_packet)
+
+def process_packet(packet):
+    print(packet.summary())
+
+
+def scrape_website(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    return [a['href'] for a in soup.find_all('a', href=True)]
+
+
+def ssh_connect(host, username, password):
+    client = paramiko.SSHClient()
+    client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
+    client.connect(host, username=username, password=password)
+    stdin, stdout, stderr = client.exec_command('ls')
+    print(stdout.read().decode())
+    client.close()
+
+def train_malware_classifier(data, labels):
+    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)
+    clf = DecisionTreeClassifier()
+    clf.fit(X_train, y_train)
+    return clf.score(X_test, y_test)
+
+def build_model():
+    model = tf.keras.Sequential([
+        tf.keras.layers.Flatten(input_shape=(28, 28)),
+        tf.keras.layers.Dense(128, activation='relu'),
+        tf.keras.layers.Dense(10, activation='softmax')
+    ])
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+
+if __name__ == "__main__":
+    target = "192.168.1.1"
+    print("Scanning ports...")
+    print(scan_ports(target))
+
+    print("Sniffing packets...")
+    sniff_packets("eth0")
+
+    print("Scraping website...")
+    print(scrape_website("http://example.com"))
+
+    print("Connecting via SSH...")
+    ssh_connect("192.168.1.2", "user", "password")
+
+    # Example data for malware detection
+    data = [[...]]  # Replace with actual data
+    labels = [...]  # Replace with actual labels
+    print("Training malware classifier...")
+    print(train_malware_classifier(data, labels))
+
+    print("Building and training deep learning model...")
+    model = build_model()
+    # Add code to train the model with data
+
+