├── .gitignore ├── docs └── daipe.jpeg ├── requirements.txt ├── .github └── ISSUE_TEMPLATE │ ├── custom.md │ ├── feature_request.md │ └── bug_report.md ├── decentralized_data_storage └── ipfs_utils │ ├── ipfs_client.py │ └── ipfs_server.py ├── cybersecurity_framework └── threat_detection │ ├── anomaly_detector.py │ └── malware_detector.py ├── node_network ├── node_manager.py └── node_communication.py ├── ai_engine └── ai_models │ ├── neural_network.py │ └── decision_tree.py ├── blockchain_integration └── smart_contracts │ ├── security_contract.py │ └── data_contract.py ├── ai_data ├── data_loader.py └── data_preprocessor.py ├── tests └── ai_engine_tests │ ├── test_decision_tree.py │ └── test_neural_network.py ├── node_utils ├── node_monitoring.py └── node_registration.py ├── blockchain_integration_tests ├── test_data_contract.py └── test_security_contract.py ├── incident_response ├── response_generator.py └── notification_system.py ├── cybersecurity_framework_tests ├── test_threat_detection.py └── test_incident_response.py ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Google App Engine generated folder 2 | appengine-generated/ 3 | -------------------------------------------------------------------------------- /docs/daipe.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KOSASIH/DAIPE/HEAD/docs/daipe.jpeg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | python==3.9.5 2 | numpy==1.21.2 3 | pandas==1.3.5 4 | websocket-client==1.3.2 5 | pi-sdk==1.2.3 # Pi Network SDK 6 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | 11 | -------------------------------------------------------------------------------- /decentralized_data_storage/ipfs_utils/ipfs_client.py: -------------------------------------------------------------------------------- 1 | import ipfshttpclient 2 | 3 | class IpfsClient: 4 | def __init__(self, host, port): 5 | self.client = ipfshttpclient.connect(host, port) 6 | 7 | def add_file(self, file_path): 8 | return self.client.add(file_path) 9 | 10 | def get_file(self, file_hash): 11 | return self.client.cat(file_hash) 12 | -------------------------------------------------------------------------------- /cybersecurity_framework/threat_detection/anomaly_detector.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn.ensemble import IsolationForest 3 | 4 | class AnomalyDetector: 5 | def __init__(self, data): 6 | self.data = data 7 | self.model = IsolationForest(contamination=0.1) 8 | 9 | def train(self): 10 | self.model.fit(self.data) 11 | 12 | def predict(self, new_data): 13 | return self.model.predict(new_data) 14 | -------------------------------------------------------------------------------- /node_network/node_manager.py: -------------------------------------------------------------------------------- 1 | import socket 2 | 3 | class NodeManager: 4 | def __init__(self, node_id, node_address): 5 | self.node_id = node_id 6 | self.node_address = node_address 7 | self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) 8 | 9 | def connect(self): 10 | self.socket.connect((self.node_address, 8080)) 11 | 12 | def send_message(self, message): 13 | self.socket.send(message.encode()) 14 | 15 | def receive_message(self): 16 | return self.socket.recv(1024).decode() 17 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /ai_engine/ai_models/neural_network.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | class NeuralNetwork: 4 | def __init__(self, input_shape, output_shape): 5 | self.model = tf.keras.models.Sequential([ 6 | tf.keras.layers.Dense(64, activation='relu', input_shape=input_shape), 7 | tf.keras.layers.Dense(32, activation='relu'), 8 | tf.keras.layers.Dense(output_shape, activation='softmax') 9 | ]) 10 | self.model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) 11 | 12 | def train(self, X, y): 13 | self.model.fit(X, y, epochs=10, batch_size=32, validation_split=0.2) 14 | 15 | def predict(self, X): 16 | return self.model.predict(X) 17 | -------------------------------------------------------------------------------- /blockchain_integration/smart_contracts/security_contract.py: -------------------------------------------------------------------------------- 1 | pragma solidity ^0.8.0; 2 | 3 | contract SecurityContract { 4 | address private owner; 5 | mapping (address => bool) public allowedNodes; 6 | 7 | constructor() public { 8 | owner = msg.sender; 9 | } 10 | 11 | function addNode(address node) public { 12 | require(msg.sender == owner, "Only the owner can add nodes"); 13 | allowedNodes[node] = true; 14 | } 15 | 16 | function removeNode(address node) public { 17 | require(msg.sender == owner, "Only the owner can remove nodes"); 18 | allowedNodes[node] = false; 19 | } 20 | 21 | function checkNode(address node) public view returns (bool) { 22 | return allowedNodes[node]; 23 | } 24 | } 25 | -------------------------------------------------------------------------------- /ai_data/data_loader.py: -------------------------------------------------------------------------------- 1 | # Data Loader class 2 | class DataLoader: 3 | def __init__(self, data_contract): 4 | self.data_contract = data_contract 5 | 6 | def load(self): 7 | # Load data from source 8 | if self.data_contract.data_type == "csv": 9 | data = pd.read_csv(self.data_contract.data_source) 10 | elif self.data_contract.data_type == "json": 11 | data = pd.read_json(self.data_contract.data_source) 12 | elif self.data_contract.data_type == "parquet": 13 | data = pd.read_parquet(self.data_contract.data_source) 14 | else: 15 | raise ValueError("Invalid data type") 16 | 17 | return data 18 | 19 | def __str__(self): 20 | return f"DataLoader(data_contract={self.data_contract})" 21 | 22 | # Example usage 23 | data_loader = DataLoader(data_contract) 24 | data = data_loader.load() 25 | print(data.head()) 26 | -------------------------------------------------------------------------------- /ai_data/data_preprocessor.py: -------------------------------------------------------------------------------- 1 | # Data Preprocessor class 2 | class DataPreprocessor: 3 | def __init__(self, data): 4 | self.data = data 5 | 6 | def preprocess(self): 7 | # Handle missing values 8 | self.data.fillna(self.data.mean(), inplace=True) 9 | 10 | # Handle outliers 11 | self.data = self.data[(np.abs(self.data) <= 3 * self.data.std()).all(axis=1)] 12 | 13 | # Encode categorical variables 14 | categorical_cols = self.data.select_dtypes(include=["object"]).columns 15 | for col in categorical_cols: 16 | self.data[col] = pd.get_dummies(self.data[col], drop_first=True) 17 | 18 | return self.data 19 | 20 | def __str__(self): 21 | return f"DataPreprocessor(data={self.data})" 22 | 23 | # Example usage 24 | data_preprocessor = DataPreprocessor(data) 25 | processed_data = data_preprocessor.preprocess() 26 | print(processed_data.head()) 27 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /tests/ai_engine_tests/test_decision_tree.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | from ai_engine.decision_tree import DecisionTree 4 | 5 | class TestDecisionTree(unittest.TestCase): 6 | def setUp(self): 7 | # Set up decision tree for testing 8 | self.dt = DecisionTree() 9 | 10 | def test_train(self): 11 | # Test training of decision tree 12 | input_data = np.random.rand(100, 10) 13 | output_data = np.random.rand(100, 1) 14 | self.dt.train(input_data, output_data) 15 | self.assertTrue(self.dt.trained) 16 | 17 | def test_predict(self): 18 | # Test prediction using decision tree 19 | input_data = np.random.rand(1, 10) 20 | output = self.dt.predict(input_data) 21 | self.assertEqual(output.shape, (1, 1)) 22 | 23 | def test_evaluate(self): 24 | # Test evaluation of decision tree 25 | input_data = np.random.rand(100, 10) 26 | output_data = np.random.rand(100, 1) 27 | accuracy = self.dt.evaluate(input_data, output_data) 28 | self.assertGreaterEqual(accuracy, 0.0) 29 | self.assertLessEqual(accuracy, 1.0) 30 | 31 | if __name__ == '__main__': 32 | unittest.main() 33 | -------------------------------------------------------------------------------- /tests/ai_engine_tests/test_neural_network.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | import numpy as np 4 | from ai_engine.neural_network import NeuralNetwork 5 | 6 | class TestNeuralNetwork(unittest.TestCase): 7 | def setUp(self): 8 | # Set up neural network for testing 9 | self.nn = NeuralNetwork(input_size=784, hidden_size=256, output_size=10) 10 | 11 | def test_forward_pass(self): 12 | # Test forward pass through neural network 13 | input_data = np.random.rand(1, 784) 14 | output = self.nn.forward_pass(input_data) 15 | self.assertEqual(output.shape, (1, 10)) 16 | 17 | def test_backward_pass(self): 18 | # Test backward pass through neural network 19 | input_data = np.random.rand(1, 784) 20 | output = self.nn.forward_pass(input_data) 21 | self.nn.backward_pass(output, input_data) 22 | self.assertTrue(self.nn.weights_updated) 23 | 24 | def test_train(self): 25 | # Test training of neural network 26 | input_data = np.random.rand(100, 784) 27 | output_data = np.random.rand(100, 10) 28 | self.nn.train(input_data, output_data, epochs=10) 29 | self.assertTrue(self.nn.trained) 30 | 31 | if __name__ == '__main__': 32 | unittest.main() 33 | -------------------------------------------------------------------------------- /node_utils/node_monitoring.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import requests 5 | import psutil 6 | 7 | # Node Monitoring class 8 | class NodeMonitoring: 9 | def __init__(self, node_id, monitoring_server_url): 10 | self.node_id = node_id 11 | self.monitoring_server_url = monitoring_server_url 12 | 13 | def monitor_node(self): 14 | # Monitor node resources and send data to monitoring server 15 | node_data = { 16 | "node_id": self.node_id, 17 | "cpu_usage": psutil.cpu_percent(), 18 | "memory_usage": psutil.virtual_memory().percent, 19 | "storage_usage": psutil.disk_usage('/').percent 20 | } 21 | response = requests.post(self.monitoring_server_url, data=json.dumps(node_data)) 22 | if response.status_code == 200: 23 | print(f"Node {self.node_id} monitoring data sent successfully") 24 | else: 25 | print(f"Error sending node {self.node_id} monitoring data: {response.text}") 26 | 27 | def start_monitoring(self): 28 | # Start monitoring node resources at regular intervals 29 | while True: 30 | self.monitor_node() 31 | time.sleep(60) # Monitor every 60 seconds 32 | 33 | # Example usage 34 | node_id = "node1" 35 | monitoring_server_url = "https://monitoring-server.com/monitor" 36 | 37 | node_monitoring = NodeMonitoring(node_id, monitoring_server_url) 38 | node_monitoring.start_monitoring() 39 | -------------------------------------------------------------------------------- /blockchain_integration_tests/test_data_contract.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | from web3 import Web3 4 | from blockchain_integration.data_contract import DataContract 5 | 6 | class TestDataContract(unittest.TestCase): 7 | def setUp(self): 8 | # Set up test blockchain network 9 | self.w3 = Web3(Web3.HTTPProvider('https://mainnet.infura.io/v3/YOUR_PROJECT_ID')) 10 | self.data_contract = DataContract(self.w3, '0x1234567890abcdef') 11 | 12 | def test_deploy(self): 13 | # Test deployment of data contract 14 | tx_hash = self.data_contract.deploy() 15 | self.assertIsNotNone(tx_hash) 16 | 17 | def test_set_data(self): 18 | # Test setting of data 19 | data = 'Hello, World!' 20 | tx_hash = self.data_contract.set_data(data) 21 | self.assertIsNotNone(tx_hash) 22 | 23 | def test_get_data(self): 24 | # Test getting of data 25 | data = self.data_contract.get_data() 26 | self.assertEqual(data, 'Hello, World!') 27 | 28 | def test_update_data(self): 29 | # Test updating of data 30 | new_data = 'Hello, Blockchain!' 31 | tx_hash = self.data_contract.update_data(new_data) 32 | self.assertIsNotNone(tx_hash) 33 | 34 | def test_delete_data(self): 35 | # Test deletion of data 36 | tx_hash = self.data_contract.delete_data() 37 | self.assertIsNotNone(tx_hash) 38 | 39 | if __name__ == '__main__': 40 | unittest.main() 41 | -------------------------------------------------------------------------------- /blockchain_integration/smart_contracts/data_contract.py: -------------------------------------------------------------------------------- 1 | # Data Contract class 2 | class DataContract: 3 | def __init__(self, data_type, data_schema, data_source, data_destination): 4 | self.data_type = data_type 5 | self.data_schema = data_schema 6 | self.data_source = data_source 7 | self.data_destination = data_destination 8 | 9 | def validate(self): 10 | # Validate data type 11 | if self.data_type not in ["csv", "json", "parquet"]: 12 | raise ValueError("Invalid data type") 13 | 14 | # Validate data schema 15 | if not isinstance(self.data_schema, dict): 16 | raise ValueError("Invalid data schema") 17 | 18 | # Validate data source 19 | if not isinstance(self.data_source, str): 20 | raise ValueError("Invalid data source") 21 | 22 | # Validate data destination 23 | if not isinstance(self.data_destination, str): 24 | raise ValueError("Invalid data destination") 25 | 26 | def to_dict(self): 27 | return { 28 | "data_type": self.data_type, 29 | "data_schema": self.data_schema, 30 | "data_source": self.data_source, 31 | "data_destination": self.data_destination 32 | } 33 | 34 | def __str__(self): 35 | return f"DataContract(data_type={self.data_type}, data_schema={self.data_schema}, data_source={self.data_source}, data_destination={self.data_destination})" 36 | 37 | # Example usage 38 | data_contract = DataContract( 39 | data_type="csv", 40 | data_schema={"column1": "int", "column2": "string"}, 41 | data_source="data.csv", 42 | data_destination="processed_data.csv" 43 | ) 44 | 45 | print(data_contract.to_dict()) 46 | -------------------------------------------------------------------------------- /incident_response/response_generator.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import requests 5 | from jinja2 import Template 6 | from datetime import datetime 7 | 8 | # Incident Response class 9 | class IncidentResponse: 10 | def __init__(self, incident_id, incident_type, severity, description, affected_systems): 11 | self.incident_id = incident_id 12 | self.incident_type = incident_type 13 | self.severity = severity 14 | self.description = description 15 | self.affected_systems = affected_systems 16 | 17 | def generate_response(self): 18 | # Load response template 19 | template = Template(filename="response_template.j2") 20 | 21 | # Render response template with incident data 22 | response = template.render( 23 | incident_id=self.incident_id, 24 | incident_type=self.incident_type, 25 | severity=self.severity, 26 | description=self.description, 27 | affected_systems=self.affected_systems, 28 | timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S") 29 | ) 30 | 31 | return response 32 | 33 | def send_response(self, notification_system): 34 | # Send response to notification system 35 | notification_system.send_notification(self.generate_response()) 36 | 37 | # Example usage 38 | incident_response = IncidentResponse( 39 | incident_id="INC123", 40 | incident_type="Malware Outbreak", 41 | severity="High", 42 | description="Malware outbreak detected on multiple systems", 43 | affected_systems=["system1", "system2", "system3"] 44 | ) 45 | 46 | notification_system = NotificationSystem() 47 | incident_response.send_response(notification_system) 48 | -------------------------------------------------------------------------------- /incident_response/notification_system.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import requests 5 | from email.mime.text import MIMEText 6 | from email.mime.multipart import MIMEMultipart 7 | from smtplib import SMTP 8 | 9 | # Notification System class 10 | class NotificationSystem: 11 | def __init__(self, notification_config): 12 | self.notification_config = notification_config 13 | 14 | def send_notification(self, response): 15 | # Load notification configuration 16 | notification_config = self.notification_config 17 | 18 | # Send notification via email 19 | msg = MIMEMultipart() 20 | msg["Subject"] = "Incident Response - " + response["incident_id"] 21 | msg["From"] = notification_config["email_from"] 22 | msg["To"] = notification_config["email_to"] 23 | 24 | body = response["response"] 25 | msg.attach(MIMEText(body, "plain")) 26 | 27 | server = SMTP(notification_config["smtp_server"], notification_config["smtp_port"]) 28 | server.starttls() 29 | server.login(notification_config["smtp_username"], notification_config["smtp_password"]) 30 | server.sendmail(notification_config["email_from"], notification_config["email_to"], msg.as_string()) 31 | server.quit() 32 | 33 | # Send notification via API 34 | api_url = notification_config["api_url"] 35 | api_key = notification_config["api_key"] 36 | headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} 37 | response = requests.post(api_url, headers=headers, json=response) 38 | if response.status_code != 200: 39 | print("Error sending notification via API") 40 | 41 | def load_notification_config(self): 42 | # Load notification configuration from file 43 | with open("notification_config.json", "r") as f: 44 | notification_config = json.load(f) 45 | return notification_config 46 | 47 | # Example usage 48 | notification_system = NotificationSystem(NotificationSystem().load_notification_config()) 49 | -------------------------------------------------------------------------------- /blockchain_integration_tests/test_security_contract.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | from web3 import Web3 4 | from blockchain_integration.security_contract import SecurityContract 5 | 6 | class TestSecurityContract(unittest.TestCase): 7 | def setUp(self): 8 | # Set up test blockchain network 9 | self.w3 = Web3(Web3.HTTPProvider('https://mainnet.infura.io/v3/YOUR_PROJECT_ID')) 10 | self.security_contract = SecurityContract(self.w3, '0x1234567890abcdef') 11 | 12 | def test_deploy(self): 13 | # Test deployment of security contract 14 | tx_hash = self.security_contract.deploy() 15 | self.assertIsNotNone(tx_hash) 16 | 17 | def test_mint(self): 18 | # Test minting of new tokens 19 | token_amount = 100 20 | tx_hash = self.security_contract.mint(token_amount) 21 | self.assertIsNotNone(tx_hash) 22 | 23 | def test_transfer(self): 24 | # Test transfer of tokens 25 | from_account = '0x1234567890abcdef' 26 | to_account = '0xfedcba9876543210' 27 | token_amount = 50 28 | tx_hash = self.security_contract.transfer(from_account, to_account, token_amount) 29 | self.assertIsNotNone(tx_hash) 30 | 31 | def test_balance_of(self): 32 | # Test balance of tokens for an account 33 | account = '0x1234567890abcdef' 34 | balance = self.security_contract.balance_of(account) 35 | self.assertGreaterEqual(balance, 0) 36 | 37 | def test_allowance(self): 38 | # Test allowance of tokens for an account 39 | owner = '0x1234567890abcdef' 40 | spender = '0xfedcba9876543210' 41 | allowance = self.security_contract.allowance(owner, spender) 42 | self.assertGreaterEqual(allowance, 0) 43 | 44 | def test_approve(self): 45 | # Test approval of tokens for an account 46 | owner = '0x1234567890abcdef' 47 | spender = '0xfedcba9876543210' 48 | token_amount = 50 49 | tx_hash = self.security_contract.approve(owner, spender, token_amount) 50 | self.assertIsNotNone(tx_hash) 51 | 52 | if __name__ == '__main__': 53 | unittest.main() 54 | -------------------------------------------------------------------------------- /cybersecurity_framework_tests/test_threat_detection.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | from cybersecurity_framework.threat_detection import ThreatDetection 4 | from cybersecurity_framework.threat_intelligence import ThreatIntelligence 5 | 6 | class TestThreatDetection(unittest.TestCase): 7 | def setUp(self): 8 | # Set up threat detection system 9 | self.threat_detection = ThreatDetection() 10 | self.threat_intelligence = ThreatIntelligence() 11 | 12 | def test_analyze_network_traffic(self): 13 | # Test analysis of network traffic 14 | network_traffic = [ 15 | {'src_ip': '192.168.1.100', 'dst_ip': '8.8.8.8', 'protocol': 'TCP'}, 16 | {'src_ip': '192.168.1.100', 'dst_ip': '1.1.1.1', 'protocol': 'UDP'} 17 | ] 18 | threats = self.threat_detection.analyze_network_traffic(network_traffic) 19 | self.assertGreaterEqual(len(threats), 0) 20 | 21 | def test_analyze_system_logs(self): 22 | # Test analysis of system logs 23 | system_logs = [ 24 | {'timestamp': '2022-01-01 12:00:00', 'log_level': 'INFO', 'message': 'Login successful'}, 25 | {'timestamp': '2022-01-01 12:00:01', 'log_level': 'WARNING', 'message': 'Suspicious activity detected'} 26 | ] 27 | threats = self.threat_detection.analyze_system_logs(system_logs) 28 | self.assertGreaterEqual(len(threats), 0) 29 | 30 | def test_integrate_with_threat_intelligence(self): 31 | # Test integration with threat intelligence 32 | threat_intel = self.threat_intelligence.get_threat_intel() 33 | self.threat_detection.integrate_with_threat_intelligence(threat_intel) 34 | self.assertTrue(self.threat_detection.threat_intel_integrated) 35 | 36 | def test_detect_malware(self): 37 | # Test detection of malware 38 | malware_sample = b'\x00\x01\x02\x03\x04\x05' 39 | detection_result = self.threat_detection.detect_malware(malware_sample) 40 | self.assertTrue(detection_result) 41 | 42 | def test_detect_anomaly(self): 43 | # Test detection of anomaly 44 | anomaly_data = [1, 2, 3, 4, 5, 6, 7, 8, 9] 45 | detection_result = self.threat_detection.detect_anomaly(anomaly_data) 46 | self.assertTrue(detection_result) 47 | 48 | if __name__ == '__main__': 49 | unittest.main() 50 | -------------------------------------------------------------------------------- /cybersecurity_framework_tests/test_incident_response.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import unittest 3 | from cybersecurity_framework.incident_response import IncidentResponse 4 | from cybersecurity_framework.threat_detection import ThreatDetection 5 | 6 | class TestIncidentResponse(unittest.TestCase): 7 | def setUp(self): 8 | # Set up incident response system 9 | self.incident_response = IncidentResponse() 10 | self.threat_detection = ThreatDetection() 11 | 12 | def test_identify_incident(self): 13 | # Test identification of incident 14 | incident_data = {'timestamp': '2022-01-01 12:00:00', 'log_level': 'CRITICAL', 'message': 'System compromise detected'} 15 | incident_id = self.incident_response.identify_incident(incident_data) 16 | self.assertIsNotNone(incident_id) 17 | 18 | def test_contain_incident(self): 19 | # Test containment of incident 20 | incident_id = 'INC12345' 21 | containment_result = self.incident_response.contain_incident(incident_id) 22 | self.assertTrue(containment_result) 23 | 24 | def test_eradicate_incident(self): 25 | # Test eradication of incident 26 | incident_id = 'INC12345' 27 | eradication_result = self.incident_response.eradicate_incident(incident_id) 28 | self.assertTrue(eradication_result) 29 | 30 | def test_recover_from_incident(self): 31 | # Test recovery from incident 32 | incident_id = 'INC12345' 33 | recovery_result = self.incident_response.recover_from_incident(incident_id) 34 | self.assertTrue(recovery_result) 35 | 36 | def test_integrate_with_threat_detection(self): 37 | # Test integration with threat detection 38 | threat_data = self.threat_detection.analyze_network_traffic([]) 39 | self.incident_response.integrate_with_threat_detection(threat_data) 40 | self.assertTrue(self.incident_response.threat_detection_integrated) 41 | 42 | def test_generate_incident_report(self): 43 | # Test generation of incident report 44 | incident_id = 'INC12345' 45 | report = self.incident_response.generate_incident_report(incident_id) 46 | self.assertIsNotNone(report) 47 | 48 | def test_notify_stakeholders(self): 49 | # Test notification of stakeholders 50 | incident_id = 'INC12345' 51 | notification_result = self.incident_response.notify_stakeholders(incident_id) 52 | self.assertTrue(notification_result) 53 | 54 | if __name__ == '__main__': 55 | unittest.main() 56 | -------------------------------------------------------------------------------- /node_utils/node_registration.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import requests 5 | from cryptography.fernet import Fernet 6 | from cryptography.hazmat.primitives import hashes 7 | from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC 8 | 9 | # Node Registration class 10 | class NodeRegistration: 11 | def __init__(self, node_id, node_key, registration_server_url): 12 | self.node_id = node_id 13 | self.node_key = node_key 14 | self.registration_server_url = registration_server_url 15 | 16 | def register_node(self): 17 | # Register node with registration server 18 | registration_data = { 19 | "node_id": self.node_id, 20 | "node_key": self.node_key, 21 | "node_type": "compute_node", 22 | "node_capabilities": ["cpu", "memory", "storage"] 23 | } 24 | encrypted_registration_data = self.encrypt_data(json.dumps(registration_data)) 25 | response = requests.post(self.registration_server_url, data=encrypted_registration_data) 26 | if response.status_code == 200: 27 | print(f"Node {self.node_id} registered successfully") 28 | else: 29 | print(f"Error registering node {self.node_id}: {response.text}") 30 | 31 | def encrypt_data(self, data): 32 | # Encrypt data using Fernet symmetric encryption 33 | kdf = PBKDF2HMAC( 34 | algorithm=hashes.SHA256(), 35 | length=32, 36 | salt=self.node_key.encode(), 37 | iterations=100000, 38 | ) 39 | key = base64.urlsafe_b64encode(kdf.derive(self.node_key.encode())) 40 | f = Fernet(key) 41 | encrypted_data = f.encrypt(data.encode()) 42 | return encrypted_data 43 | 44 | def decrypt_data(self, encrypted_data): 45 | # Decrypt data using Fernet symmetric encryption 46 | kdf = PBKDF2HMAC( 47 | algorithm=hashes.SHA256(), 48 | length=32, 49 | salt=self.node_key.encode(), 50 | iterations=100000, 51 | ) 52 | key = base64.urlsafe_b64encode(kdf.derive(self.node_key.encode())) 53 | f = Fernet(key) 54 | decrypted_data = f.decrypt(encrypted_data) 55 | return decrypted_data.decode() 56 | 57 | # Example usage 58 | node_id = "node1" 59 | node_key = "my_secret_key" 60 | registration_server_url = "https://registration-server.com/register" 61 | 62 | node_registration = NodeRegistration(node_id, node_key, registration_server_url) 63 | node_registration.register_node() 64 | -------------------------------------------------------------------------------- /decentralized_data_storage/ipfs_utils/ipfs_server.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import ipfshttpclient 5 | from flask import Flask, request, jsonify 6 | from flask_cors import CORS 7 | from ipfsapi import IpfsApi 8 | 9 | # IPFS Server class 10 | class IpfsServer: 11 | def __init__(self, ipfs_api, ipfs_http_client): 12 | self.ipfs_api = ipfs_api 13 | self.ipfs_http_client = ipfs_http_client 14 | 15 | def add_file(self, file_path): 16 | # Add file to IPFS 17 | with open(file_path, "rb") as f: 18 | file_hash = self.ipfs_api.add(f.read()) 19 | return file_hash 20 | 21 | def get_file(self, file_hash): 22 | # Get file from IPFS 23 | file_data = self.ipfs_api.cat(file_hash) 24 | return file_data 25 | 26 | def pin_file(self, file_hash): 27 | # Pin file to IPFS 28 | self.ipfs_api.pin_add(file_hash) 29 | 30 | def unpin_file(self, file_hash): 31 | # Unpin file from IPFS 32 | self.ipfs_api.pin_rm(file_hash) 33 | 34 | def get_file_info(self, file_hash): 35 | # Get file info from IPFS 36 | file_info = self.ipfs_api.object_stat(file_hash) 37 | return file_info 38 | 39 | def search_files(self, query): 40 | # Search files in IPFS 41 | search_results = self.ipfs_api.search(query) 42 | return search_results 43 | 44 | # Flask API 45 | app = Flask(__name__) 46 | CORS(app) 47 | 48 | # IPFS API client 49 | ipfs_api = IpfsApi() 50 | 51 | # IPFS HTTP client 52 | ipfs_http_client = ipfshttpclient.connect() 53 | 54 | # IPFS Server instance 55 | ipfs_server = IpfsServer(ipfs_api, ipfs_http_client) 56 | 57 | # API endpoints 58 | @app.route("/add_file", methods=["POST"]) 59 | def add_file(): 60 | file_path = request.form["file_path"] 61 | file_hash = ipfs_server.add_file(file_path) 62 | return jsonify({"file_hash": file_hash}) 63 | 64 | @app.route("/get_file", methods=["GET"]) 65 | def get_file(): 66 | file_hash = request.args.get("file_hash") 67 | file_data = ipfs_server.get_file(file_hash) 68 | return jsonify({"file_data": file_data}) 69 | 70 | @app.route("/pin_file", methods=["POST"]) 71 | def pin_file(): 72 | file_hash = request.form["file_hash"] 73 | ipfs_server.pin_file(file_hash) 74 | return jsonify({"message": "File pinned successfully"}) 75 | 76 | @app.route("/unpin_file", methods=["POST"]) 77 | def unpin_file(): 78 | file_hash = request.form["file_hash"] 79 | ipfs_server.unpin_file(file_hash) 80 | return jsonify({"message": "File unpinned successfully"}) 81 | 82 | @app.route("/get_file_info", methods=["GET"]) 83 | def get_file_info(): 84 | file_hash = request.args.get("file_hash") 85 | file_info = ipfs_server.get_file_info(file_hash) 86 | return jsonify({"file_info": file_info}) 87 | 88 | @app.route("/search_files", methods=["GET"]) 89 | def search_files(): 90 | query = request.args.get("query") 91 | search_results = ipfs_server.search_files(query) 92 | return jsonify({"search_results": search_results}) 93 | 94 | if __name__ == "__main__": 95 | app.run(debug=True) 96 | -------------------------------------------------------------------------------- /ai_engine/ai_models/decision_tree.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | from sklearn.tree import DecisionTreeClassifier 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix 5 | import pandas as pd 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from sklearn import tree 9 | 10 | # Function to import the dataset 11 | def importdata(): 12 | balance_data = pd.read_csv( 13 | 'https://archive.ics.uci.edu/ml/machine-learning-' + 14 | 'databases/balance-scale/balance-scale.data', 15 | sep=',', header=None) 16 | # Displaying dataset information 17 | print("Dataset Length: ", len(balance_data)) 18 | print("Dataset Shape: ", balance_data.shape) 19 | print("Dataset: ", balance_data.head()) 20 | return balance_data 21 | 22 | # Function to split the dataset into features and target variables 23 | def splitdataset(balance_data): 24 | # Separating the target variable 25 | X = balance_data.values[:, 1:5] 26 | Y = balance_data.values[:, 0] 27 | # Splitting the dataset into train and test 28 | X_train, X_test, y_train, y_test = train_test_split( 29 | X, Y, test_size=0.3, random_state=100) 30 | return X, Y, X_train, X_test, y_train, y_test 31 | 32 | # Function to train using Gini Index 33 | def train_using_gini(X_train, X_test, y_train): 34 | # Creating the classifier object 35 | clf_gini = DecisionTreeClassifier(criterion="gini", random_state=100, 36 | max_depth=3, min_samples_leaf=5) 37 | # Performing training 38 | clf_gini.fit(X_train, y_train) 39 | return clf_gini 40 | 41 | # Function to train using Entropy 42 | def train_using_entropy(X_train, X_test, y_train): 43 | # Decision tree with entropy 44 | clf_entropy = DecisionTreeClassifier( 45 | criterion="entropy", random_state=100, max_depth=3, min_samples_leaf=5) 46 | # Performing training 47 | clf_entropy.fit(X_train, y_train) 48 | return clf_entropy 49 | 50 | # Function to make predictions 51 | def prediction(X_test, clf_object): 52 | y_pred = clf_object.predict(X_test) 53 | print("Predicted values:") 54 | print(y_pred) 55 | return y_pred 56 | 57 | # Function to calculate accuracy 58 | def cal_accuracy(y_test, y_pred): 59 | print("Confusion Matrix: ", confusion_matrix(y_test, y_pred)) 60 | print("Accuracy : ", accuracy_score(y_test, y_pred)*100) 61 | print("Report : ", classification_report(y_test, y_pred)) 62 | 63 | # Driver code 64 | def main(): 65 | data = importdata() 66 | X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 67 | clf_gini = train_using_gini(X_train, X_test, y_train) 68 | clf_entropy = train_using_entropy(X_train, X_test, y_train) 69 | print("Results Using Gini Index:") 70 | y_pred_gini = prediction(X_test, clf_gini) 71 | cal_accuracy(y_test, y_pred_gini) 72 | print("Results Using Entropy:") 73 | y_pred_entropy = prediction(X_test, clf_entropy) 74 | cal_accuracy(y_test, y_pred_entropy) 75 | 76 | # Plotting the decision tree 77 | plt.figure(figsize=(15, 10)) 78 | tree.plot_tree(clf_gini, filled=True, feature_names=[ 79 | 'X1', 'X2', 'X3', 'X4'], class_names=['L', 'B', 'R']) 80 | plt.show() 81 | 82 | if __name__ == "__main__": 83 | main() 84 | -------------------------------------------------------------------------------- /node_network/node_communication.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import json 4 | import socket 5 | import threading 6 | import time 7 | from cryptography.fernet import Fernet 8 | from cryptography.hazmat.primitives import hashes 9 | from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC 10 | 11 | # Node Communication class 12 | class NodeCommunication: 13 | def __init__(self, node_id, node_key, node_port, network_nodes): 14 | self.node_id = node_id 15 | self.node_key = node_key 16 | self.node_port = node_port 17 | self.network_nodes = network_nodes 18 | self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) 19 | self.socket.bind(("localhost", self.node_port)) 20 | self.socket.listen(5) 21 | self.threads = [] 22 | 23 | def start_node(self): 24 | # Start node and listen for incoming connections 25 | print(f"Node {self.node_id} started and listening on port {self.node_port}") 26 | while True: 27 | conn, addr = self.socket.accept() 28 | print(f"Connected by {addr}") 29 | t = threading.Thread(target=self.handle_connection, args=(conn,)) 30 | t.start() 31 | self.threads.append(t) 32 | 33 | def handle_connection(self, conn): 34 | # Handle incoming connection and receive data 35 | while True: 36 | data = conn.recv(1024) 37 | if not data: 38 | break 39 | print(f"Received data from {addr}: {data.decode()}") 40 | self.process_data(data.decode()) 41 | 42 | def process_data(self, data): 43 | # Process received data and take action 44 | data_json = json.loads(data) 45 | if data_json["type"] == "message": 46 | print(f"Received message from node {data_json['node_id']}: {data_json['message']}") 47 | self.send_response(data_json["node_id"], "ack") 48 | elif data_json["type"] == "request": 49 | print(f"Received request from node {data_json['node_id']}: {data_json['request']}") 50 | self.send_response(data_json["node_id"], "response") 51 | 52 | def send_response(self, node_id, response): 53 | # Send response to node 54 | response_json = json.dumps({"type": "response", "node_id": self.node_id, "response": response}) 55 | self.send_data(node_id, response_json) 56 | 57 | def send_data(self, node_id, data): 58 | # Send data to node 59 | node_port = self.network_nodes[node_id]["port"] 60 | conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM) 61 | conn.connect(("localhost", node_port)) 62 | conn.sendall(data.encode()) 63 | conn.close() 64 | 65 | def encrypt_data(self, data): 66 | # Encrypt data using Fernet symmetric encryption 67 | kdf = PBKDF2HMAC( 68 | algorithm=hashes.SHA256(), 69 | length=32, 70 | salt=self.node_key.encode(), 71 | iterations=100000, 72 | ) 73 | key = base64.urlsafe_b64encode(kdf.derive(self.node_key.encode())) 74 | f = Fernet(key) 75 | encrypted_data = f.encrypt(data.encode()) 76 | return encrypted_data 77 | 78 | def decrypt_data(self, encrypted_data): 79 | # Decrypt data using Fernet symmetric encryption 80 | kdf = PBKDF2HMAC( 81 | algorithm=hashes.SHA256(), 82 | length=32, 83 | salt=self.node_key.encode(), 84 | iterations=100000, 85 | ) 86 | key = base64.urlsafe_b64encode(kdf.derive(self.node_key.encode())) 87 | f = Fernet(key) 88 | decrypted_data = f.decrypt(encrypted_data) 89 | return decrypted_data.decode() 90 | 91 | # Example usage 92 | node_id = "node1" 93 | node_key = "my_secret_key" 94 | node_port = 8080 95 | network_nodes = { 96 | "node1": {"port": 8080}, 97 | "node2": {"port": 8081}, 98 | "node3": {"port": 8082}, 99 | } 100 | 101 | node_communication = NodeCommunication(node_id, node_key, node_port, network_nodes) 102 | node_communication.start_node() 103 | -------------------------------------------------------------------------------- /cybersecurity_framework/threat_detection/malware_detector.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import hashlib 4 | import pefile 5 | import pefile 6 | import yara 7 | import json 8 | import requests 9 | from sklearn.ensemble import RandomForestClassifier 10 | from sklearn.model_selection import train_test_split 11 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix 12 | import pandas as pd 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | 16 | # Function to extract features from a PE file 17 | def extract_features(file_path): 18 | features = [] 19 | pe = pefile.PE(file_path) 20 | features.append(pe.FILE_HEADER.NumberOfSections) 21 | features.append(pe.FILE_HEADER.SizeOfOptionalHeader) 22 | features.append(pe.OPTIONAL_HEADER.SizeOfCode) 23 | features.append(pe.OPTIONAL_HEADER.SizeOfInitializedData) 24 | features.append(pe.OPTIONAL_HEADER.SizeOfUninitializedData) 25 | features.append(pe.OPTIONAL_HEADER.AddressOfEntryPoint) 26 | features.append(pe.OPTIONAL_HEADER.BaseOfCode) 27 | features.append(pe.OPTIONAL_HEADER.BaseOfData) 28 | features.append(pe.OPTIONAL_HEADER.ImageBase) 29 | features.append(pe.OPTIONAL_HEADER.SectionAlignment) 30 | features.append(pe.OPTIONAL_HEADER.FileAlignment) 31 | features.append(pe.OPTIONAL_HEADER.SizeOfHeaders) 32 | features.append(pe.OPTIONAL_HEADER.CheckSum) 33 | features.append(pe.OPTIONAL_HEADER.SizeOfImage) 34 | features.append(pe.OPTIONAL_HEADER.SizeOfHeaders) 35 | features.append(pe.OPTIONAL_HEADER.Subsystem) 36 | features.append(pe.OPTIONAL_HEADER.DllCharacteristics) 37 | features.append(pe.OPTIONAL_HEADER.SizeOfStackReserve) 38 | features.append(pe.OPTIONAL_HEADER.SizeOfStackCommit) 39 | features.append(pe.OPTIONAL_HEADER.SizeOfHeapReserve) 40 | features.append(pe.OPTIONAL_HEADER.SizeOfHeapCommit) 41 | features.append(pe.OPTIONAL_HEADER.LoaderFlags) 42 | features.append(pe.OPTIONAL_HEADER.NumberOfRvaAndSizes) 43 | features.append(pe.FILE_HEADER.Characteristics) 44 | features.append(pe.FILE_HEADER.Machine) 45 | features.append(pe.FILE_HEADER.NumberOfSections) 46 | features.append(pe.FILE_HEADER.TimeDateStamp) 47 | features.append(pe.FILE_HEADER.PointerToSymbolTable) 48 | features.append(pe.FILE_HEADER.NumberOfSymbols) 49 | features.append(pe.FILE_HEADER.SizeOfOptionalHeader) 50 | features.append(pe.FILE_HEADER.ImageBase) 51 | return features 52 | 53 | # Function to calculate the hash of a file 54 | def calculate_hash(file_path): 55 | hash_sha256 = hashlib.sha256() 56 | with open(file_path, "rb") as f: 57 | for chunk in iter(lambda: f.read(4096), b""): 58 | hash_sha256.update(chunk) 59 | return hash_sha256.hexdigest() 60 | 61 | # Function to scan a file using YARA rules 62 | def scan_with_yara(file_path): 63 | rules = yara.compile(file="malware_rules.yara") 64 | matches = rules.match(file_path) 65 | if matches: 66 | return True 67 | else: 68 | return False 69 | 70 | # Function to classify a file as malicious or benign using a machine learning model 71 | def classify_file(file_path): 72 | features = extract_features(file_path) 73 | features = np.array(features).reshape(1, -1) 74 | model = RandomForestClassifier() 75 | model.load("malware_model.pkl") 76 | prediction = model.predict(features) 77 | if prediction[0] == 0: 78 | return "Benign" 79 | else: 80 | return "Malicious" 81 | 82 | # Function to submit a file to a sandbox for analysis 83 | def submit_to_sandbox(file_path): 84 | api_key = "YOUR_API_KEY" 85 | api_secret = "YOUR_API_SECRET" 86 | url = "https://sandbox.example.com/api/submit" 87 | headers = { 88 | "Authorization": f"Bearer {api_key}", 89 | "Content-Type": "application/octet-stream" 90 | } 91 | with open(file_path, "rb") as f: 92 | response = requests.post(url, headers=headers, data=f.read()) 93 | if response.status_code == 200: 94 | return response.json() 95 | else: 96 | return None 97 | 98 | # Main function 99 | def main(): 100 | file_path = "malware_sample.exe" 101 | hash = calculate_hash(file_path) 102 | print(f"File hash: {hash}") 103 | yara_result = scan_with_yara(file_path) 104 | print(f"YARA result: {yara_result}") 105 | classification = classify_file(file_path) 106 | print(f"Classification: {classification}") 107 | sandbox_result = submit_to_sandbox(file_path) 108 | print(f"Sandbox result: {sandbox_result}") 109 | 110 | if __name__ == "__main__": 111 | main() 112 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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DAIPE by KOSASIH is licensed under Creative Commons Attribution 4.0 International

151 | 152 | # DAIPE 153 | Decentralized Artificial Intelligence for Pi Network's Ecosystem 154 | 155 | DAIPE: Decentralized Artificial Intelligence for Pi Network's Ecosystem 156 | ============================================================ 157 | 158 | **Objective** 159 | ----------- 160 | 161 | Develop a decentralized, AI-driven platform that integrates with Pi Network's blockchain to enhance its ecosystem's security, scalability, and usability. 162 | 163 | **Overview** 164 | ------------ 165 | 166 | DAIPE is a decentralized platform that leverages artificial intelligence and machine learning to improve the security, scalability, and usability of Pi Network's ecosystem. The platform is designed to be modular, scalable, and highly available, ensuring that it can handle the growing demands of the Pi Network community. 167 | 168 | **Features** 169 | ------------ 170 | 171 | * **Decentralized AI-driven threat detection**: Utilize machine learning algorithms to detect and prevent threats to the Pi Network ecosystem. 172 | * **Scalable architecture**: Designed to handle high volumes of traffic and transactions, ensuring the platform remains responsive and efficient. 173 | * **Usability enhancements**: Provide a user-friendly interface for Pi Network users to interact with the platform and access its features. 174 | * **Integration with Pi Network blockchain**: Seamlessly integrate with the Pi Network blockchain to leverage its security and scalability. 175 | 176 | **Technical Requirements** 177 | ------------------------- 178 | 179 | * Python 3.9.5 180 | * NumPy 1.21.2 181 | * pandas 1.3.5 182 | * scikit-learn 1.0.2 183 | * TensorFlow 2.7.0 184 | * Keras 2.7.0 185 | * pycryptodome 3.14.1 186 | * requests 2.27.1 187 | * websocket-client 1.3.2 188 | * pi-sdk 1.2.3 189 | 190 | **Getting Started** 191 | ------------------- 192 | 193 | 1. Clone the repository: `git clone https://github.com/KOSASIH/DAIPE.git` 194 | 2. Install the dependencies: `pip install -r requirements.txt` 195 | 3. Run the platform: `python main.py` 196 | 197 | **Contributing** 198 | -------------- 199 | 200 | Contributions are welcome! If you'd like to contribute to the DAIPE project, please fork the repository, make your changes, and submit a pull request. 201 | 202 | **License** 203 | --------- 204 | 205 | DAIPE is licensed under the Apache 2.0 License. See [LICENSE](LICENSE) for details. 206 | 207 | **Acknowledgments** 208 | ---------------- 209 | 210 | * Pi Network: For providing the blockchain infrastructure and community support. 211 | * Open-source contributors: For their contributions to the development of the platform. 212 | --------------------------------------------------------------------------------