SentinelSecureSuite

                                    
# Beispiel für eine SentinelMail Filterregel
def analyze_email(email):
    threat_score = 0
    
    # Linguistische Analyse
    urgency_keywords = ["dringend", "sofort", "vertraulich"]
    if any(keyword in email.body.lower() for keyword in urgency_keywords):
        threat_score += 20
    
    # Absender-Verifikation
    if not verify_dkim(email.headers):
        threat_score += 30
    
    # Anhang-Analyse
    for attachment in email.attachments:
        if attachment.is_executable and not attachment.is_expected:
            threat_score += 50
        elif attachment.has_macros:
            threat_score += 40
    
    # Verhaltensmuster
    if email.sender not in expected_senders and email.recipient in high_value_targets:
        threat_score += 35
    
    # SentinelAI Integration
    ai_analysis = sentinel_ai.analyze_email_context(email)
    threat_score += ai_analysis.get('threat_score', 0)
    
    return threat_score
                                    
                                

                                    
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model

# Autoencoder für Anomalieerkennung in Netzwerkverkehr
class AnomalyDetector(Model):
    def __init__(self, input_dim):
        super(AnomalyDetector, self).__init__()
        self.encoder = tf.keras.Sequential([
            Input(shape=(input_dim,)),
            Dense(64, activation='relu'),
            Dense(32, activation='relu'),
            Dense(16, activation='relu')
        ])
        
        self.decoder = tf.keras.Sequential([
            Dense(32, activation='relu'),
            Dense(64, activation='relu'),
            Dense(input_dim, activation='sigmoid')
        ])
    
    def call(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded
    
    def reconstruct_error(self, x):
        reconstructed = self.call(x)
        return tf.reduce_mean(tf.square(x - reconstructed), axis=1)

# Modell initialisieren und trainieren
input_dim = 50  # Anzahl der Features im Netzwerkverkehr
model = AnomalyDetector(input_dim)
model.compile(optimizer='adam', loss='mse')

# Trainingsdaten (normaler Verkehr)
normal_traffic = load_normal_traffic()
model.fit(normal_traffic, normal_traffic, epochs=20, batch_size=32)

# Anomalie-Erkennung
def detect_anomalies(traffic_data):
    errors = model.reconstruct_error(traffic_data)
    threshold = np.percentile(errors, 95)  # 95. Perzentil als Schwellenwert
    anomalies = errors > threshold
    return anomalies, errors