diff --git a/app.py b/app.py
index 40d2245..7c9634b 100644
--- a/app.py
+++ b/app.py
@@ -53,6 +53,7 @@
 from modules.advanced_device_control import AdvancedDeviceControl
 
 import pika
+from kafka import KafkaProducer, KafkaConsumer
 
 pn.extension(design="bootstrap", sizing_mode="stretch_width")
 
@@ -548,3 +549,33 @@ def send_message_to_queue(message):
 
 # Example usage of sending a message to the queue
 send_message_to_queue("Test message")
+
+def setup_kafka():
+    try:
+        producer = KafkaProducer(bootstrap_servers='localhost:9092')
+        consumer = KafkaConsumer('my_topic', bootstrap_servers='localhost:9092', auto_offset_reset='earliest', enable_auto_commit=True, group_id='my-group')
+        return producer, consumer
+    except Exception as e:
+        logging.error(f"Error setting up Kafka: {e}")
+        return None, None
+
+def send_message_to_kafka(producer, topic, message):
+    try:
+        producer.send(topic, message.encode('utf-8'))
+        producer.flush()
+        logging.info(f"Sent message to Kafka topic {topic}: {message}")
+    except Exception as e:
+        logging.error(f"Error sending message to Kafka: {e}")
+
+def receive_message_from_kafka(consumer):
+    try:
+        for message in consumer:
+            logging.info(f"Received message from Kafka: {message.value.decode('utf-8')}")
+    except Exception as e:
+        logging.error(f"Error receiving message from Kafka: {e}")
+
+if __name__ == "__main__":
+    producer, consumer = setup_kafka()
+    if producer and consumer:
+        send_message_to_kafka(producer, 'my_topic', 'Test Kafka message')
+        receive_message_from_kafka(consumer)
diff --git a/chatbot/app.py b/chatbot/app.py
index 6d70ffd..c3207e7 100644
--- a/chatbot/app.py
+++ b/chatbot/app.py
@@ -33,6 +33,8 @@
 from modules.wireless_exploitation import WirelessExploitation
 from modules.zero_day_exploits import ZeroDayExploits
 
+from kafka import KafkaProducer, KafkaConsumer
+
 app = Flask(__name__)
 
 DATABASE_URL = "sqlite:///document_analysis.db"
@@ -357,8 +359,37 @@ def callback(ch, method, properties, body):
     except Exception as e:
         print(f"Error receiving message: {e}")
 
+def setup_kafka():
+    try:
+        producer = KafkaProducer(bootstrap_servers='localhost:9092')
+        consumer = KafkaConsumer('my_topic', bootstrap_servers='localhost:9092', auto_offset_reset='earliest', enable_auto_commit=True, group_id='my-group')
+        return producer, consumer
+    except Exception as e:
+        print(f"Error setting up Kafka: {e}")
+        return None, None
+
+def send_message_to_kafka(producer, topic, message):
+    try:
+        producer.send(topic, message.encode('utf-8'))
+        producer.flush()
+        print(f"Sent message to Kafka topic {topic}: {message}")
+    except Exception as e:
+        print(f"Error sending message to Kafka: {e}")
+
+def receive_message_from_kafka(consumer):
+    try:
+        for message in consumer:
+            print(f"Received message from Kafka: {message.value.decode('utf-8')}")
+    except Exception as e:
+        print(f"Error receiving message from Kafka: {e}")
+
 if __name__ == "__main__":
     channel = setup_message_queue()
     if channel:
         send_message(channel, "Test message")
         receive_message(channel)
+
+    producer, consumer = setup_kafka()
+    if producer and consumer:
+        send_message_to_kafka(producer, 'my_topic', 'Test Kafka message')
+        receive_message_from_kafka(consumer)
diff --git a/chatbot/chatbot.py b/chatbot/chatbot.py
index d53e2ec..1171d12 100644
--- a/chatbot/chatbot.py
+++ b/chatbot/chatbot.py
@@ -47,6 +47,7 @@
 from modules.advanced_device_control import AdvancedDeviceControl
 
 import pika
+from kafka import KafkaProducer, KafkaConsumer
 
 DATABASE_URL = "sqlite:///document_analysis.db"
 engine = create_engine(DATABASE_URL)
@@ -117,6 +118,30 @@ def handle_exploit_deployment(target):
         print(f"Error during exploit deployment: {e}")
         return "Exploit deployment failed."
 
+def setup_kafka():
+    try:
+        producer = KafkaProducer(bootstrap_servers='localhost:9092')
+        consumer = KafkaConsumer('my_topic', bootstrap_servers='localhost:9092', auto_offset_reset='earliest', enable_auto_commit=True, group_id='my-group')
+        return producer, consumer
+    except Exception as e:
+        print(f"Error setting up Kafka: {e}")
+        return None, None
+
+def send_message_to_kafka(producer, topic, message):
+    try:
+        producer.send(topic, message.encode('utf-8'))
+        producer.flush()
+        print(f"Sent message to Kafka topic {topic}: {message}")
+    except Exception as e:
+        print(f"Error sending message to Kafka: {e}")
+
+def receive_message_from_kafka(consumer):
+    try:
+        for message in consumer:
+            print(f"Received message from Kafka: {message.value.decode('utf-8')}")
+    except Exception as e:
+        print(f"Error receiving message from Kafka: {e}")
+
 def chat():
     """Main chat function to interact with users."""
     print("Welcome to the Corporate Device Security Audit Chatbot!")
diff --git a/dashboard/dashboard.py b/dashboard/dashboard.py
index 4af47d9..be4b773 100644
--- a/dashboard/dashboard.py
+++ b/dashboard/dashboard.py
@@ -43,6 +43,7 @@
 from sqlalchemy.orm import sessionmaker
 import logging
 import pika
+from kafka import KafkaProducer, KafkaConsumer
 
 app = Flask(__name__)
 app.secret_key = 'your_secret_key'
@@ -353,5 +354,32 @@ def send_message_to_queue(message):
 # Example usage of sending a message to the queue
 send_message_to_queue("Test message")
 
+def setup_kafka():
+    try:
+        producer = KafkaProducer(bootstrap_servers='localhost:9092')
+        consumer = KafkaConsumer('my_topic', bootstrap_servers='localhost:9092', auto_offset_reset='earliest', enable_auto_commit=True, group_id='my-group')
+        return producer, consumer
+    except Exception as e:
+        logging.error(f"Error setting up Kafka: {e}")
+        return None, None
+
+def send_message_to_kafka(producer, topic, message):
+    try:
+        producer.send(topic, message.encode('utf-8'))
+        producer.flush()
+        logging.info(f"Sent message to Kafka topic {topic}: {message}")
+    except Exception as e:
+        logging.error(f"Error sending message to Kafka: {e}")
+
+def receive_message_from_kafka(consumer):
+    try:
+        for message in consumer:
+            logging.info(f"Received message from Kafka: {message.value.decode('utf-8')}")
+    except Exception as e:
+        logging.error(f"Error receiving message from Kafka: {e}")
+
 if __name__ == "__main__":
-    app.run(debug=True)
+    producer, consumer = setup_kafka()
+    if producer and consumer:
+        send_message_to_kafka(producer, 'my_topic', 'Test Kafka message')
+        receive_message_from_kafka(consumer)
diff --git a/modules/real_time_monitoring.py b/modules/real_time_monitoring.py
index bf989a2..96cf9cc 100644
--- a/modules/real_time_monitoring.py
+++ b/modules/real_time_monitoring.py
@@ -2,12 +2,14 @@
 import asyncio
 import logging
 from modules.blockchain_logger import BlockchainLogger
+from modules.machine_learning import MachineLearningModel
 
 class RealTimeMonitoring:
     def __init__(self, threat_intelligence_module):
         self.threat_intelligence_module = threat_intelligence_module
         self.alert_threshold = 0.8  # Threshold for triggering alerts
         self.blockchain_logger = BlockchainLogger()
+        self.ml_model = MachineLearningModel()
 
     async def monitor_exfiltration(self, data_stream):
         async for data in data_stream:
@@ -20,11 +22,8 @@ def detect_anomaly(self, data):
         return anomaly_score > self.alert_threshold
 
     def calculate_anomaly_score(self, data):
-        # Example anomaly detection logic using statistical methods
-        mean = sum(data) / len(data)
-        variance = sum((x - mean) ** 2 for x in data) / len(data)
-        anomaly_score = (data[-1] - mean) / (variance ** 0.5)
-        return anomaly_score
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
 
     def trigger_alert(self, data):
         # Implement alerting logic
@@ -98,3 +97,1086 @@ def calculate_dynamic_threshold(self):
     def get_system_load(self):
         # Placeholder logic to get system load
         return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib.SMTP("smtp.example.com") as server:
+                server.login("username", "password")
+                server.sendmail(sender, [recipient], msg.as_string())
+        except Exception as e:
+            logging.error(f"Failed to send alert email: {e}")
+
+    async def update_exfiltration_techniques(self):
+        latest_threats = await self.threat_intelligence_module.get_latest_threats()
+        analyzed_threats = self.threat_intelligence_module.analyze_threats(latest_threats)
+        # Implement updating exfiltration techniques with analyzed threats
+        updated_techniques = self.generate_exfiltration_techniques(analyzed_threats)
+        return updated_techniques
+
+    def generate_exfiltration_techniques(self, threats):
+        # Example logic to generate exfiltration techniques based on analyzed threats
+        techniques = []
+        for threat in threats:
+            if threat["severity"] > 0.9:
+                techniques.append("Advanced Covert Channel")
+            elif threat["severity"] > 0.7:
+                techniques.append("DNS Tunneling")
+            else:
+                techniques.append("HTTP Exfiltration")
+        return techniques
+
+    async def monitor_network_traffic(self, network_stream):
+        async for packet in network_stream:
+            if self.detect_anomaly(packet):
+                self.trigger_alert(packet)
+
+    def optimize_performance(self):
+        # Implement performance optimization logic
+        logging.info("Optimizing performance of RealTimeMonitoring module")
+        # Example: Adjust alert threshold based on system load
+        self.alert_threshold = self.calculate_dynamic_threshold()
+
+    def calculate_dynamic_threshold(self):
+        # Example logic to calculate dynamic alert threshold
+        system_load = self.get_system_load()
+        if system_load > 0.8:
+            return 0.9
+        elif system_load > 0.5:
+            return 0.85
+        else:
+            return 0.8
+
+    def get_system_load(self):
+        # Placeholder logic to get system load
+        return 0.6
+
+    async def monitor_exfiltration(self, data_stream):
+        async for data in data_stream:
+            if self.detect_anomaly(data):
+                self.trigger_alert(data)
+
+    def detect_anomaly(self, data):
+        # Implement anomaly detection logic
+        anomaly_score = self.calculate_anomaly_score(data)
+        return anomaly_score > self.alert_threshold
+
+    def calculate_anomaly_score(self, data):
+        # Example anomaly detection logic using machine learning model
+        return self.ml_model.predict(data)
+
+    def trigger_alert(self, data):
+        # Implement alerting logic
+        alert_message = f"Suspicious activity detected: {data}"
+        logging.warning(alert_message)
+        self.send_alert(alert_message)
+        self.blockchain_logger.log_event(alert_message)
+
+    def send_alert(self, message):
+        # Example alerting logic using email
+        import smtplib
+        from email.mime.text import MIMEText
+
+        sender = "alert@example.com"
+        recipient = "admin@example.com"
+        subject = "Security Alert"
+        body = message
+
+        msg = MIMEText(body)
+        msg["Subject"] = subject
+        msg["From"] = sender
+        msg["To"] = recipient
+
+        try:
+            with smtplib