Upload 8 files

README.md

@@ -1,14 +1,47 @@
----
-title: CyberSecAI
-emoji: 🏆
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.6.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: AI powered CyberSec Platform
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Cybersecurity AI Platform
+
+## Overview
+
+The Cybersecurity AI Platform is a comprehensive solution designed to harness the power of advanced open-source AI models, including large language models (LLMs), small language models (SLMs), and cybersecurity-specific AI models available on the Hugging Face Hub. The platform provides robust capabilities for threat detection, anomaly detection, and vulnerability assessment, making it an essential tool for cybersecurity professionals.
+
+## Features
+
+- **Integration of advanced AI models**: Utilize state-of-the-art AI models such as `gpt-3`, `bert-base-uncased`, `roberta-large`, `distilbert-base-uncased`, `albert-base-v2`, `tinybert`, `cybersecurity-bert`, `malware-detection-bert`, and `phishing-detection-bert`.
+- **User-friendly interface with Gradio**: Develop an intuitive and user-friendly interface using Gradio to interact with the AI models, providing various input options such as text, file uploads, and real-time data streams for analysis.
+- **Real-time monitoring and alerting**: Implement real-time monitoring capabilities to continuously analyze network traffic and system logs for potential threats, with customizable alert thresholds and notification methods.
+- **Modular architecture**: Implement a modular architecture to allow easy integration and updating of AI models, input processing modules, and alerting and monitoring modules.
+- **Integration with external threat intelligence feeds**: Fetch real-time data on emerging threats, vulnerabilities, and attack patterns from external threat intelligence services and open-source platforms like MISP and OpenCTI.
+- **User customization options**: Allow users to set specific thresholds for different types of alerts, customize notification methods, and enable or disable specific AI models based on their requirements.
+
+## Setup and Deployment
+
+To set up and deploy the Cybersecurity AI Platform on Hugging Face Spaces, follow these steps:
+
+1. **Create a Hugging Face account**: Sign up for a Hugging Face account if you don't already have one.
+2. **Set up a new Space**: Go to the Hugging Face Spaces page and create a new Space. Choose the appropriate template for your project (e.g., Gradio).
+3. **Clone the repository**: Clone the `githubnext/workspace-blank` repository to your local machine.
+4. **Install dependencies**: Ensure you have all the necessary dependencies installed, including Gradio and the required AI models from Hugging Face Hub.
+5. **Develop the platform**: Implement the cybersecurity AI platform as described in the task, including integrating AI models, creating the Gradio interface, and setting up real-time monitoring and alerting.
+6. **Test the platform**: Thoroughly test the platform to ensure it works as expected and meets the requirements.
+7. **Push the code to Hugging Face**: Push your code to the newly created Space on Hugging Face. This will automatically deploy your platform.
+8. **Monitor and maintain**: Regularly monitor the platform for any issues and update it as needed to ensure optimal performance and security.
+
+## Usage Examples
+
+### Interacting with the Platform using Gradio
+
+The Gradio interface provides an intuitive and user-friendly way to interact with the Cybersecurity AI Platform. Here are some examples of how to use the platform:
+
+1. **Text Input**: Enter text for analysis, such as threat intelligence reports, system logs, or email content. The platform will process the input and display the results, including visualizations and detailed reports.
+2. **File Uploads**: Upload files for analysis, including log files, configuration files, or email attachments. The platform will analyze the files and provide insights into potential threats or vulnerabilities.
+3. **Real-time Data Streams**: Input real-time data streams, such as network traffic, for continuous monitoring and analysis. The platform will provide real-time alerts and visualizations to help you stay on top of potential threats.
+
+### Customizing the Platform
+
+The Cybersecurity AI Platform offers various customization options to meet your specific security requirements:
+
+- **Alert Settings**: Set specific thresholds for different types of alerts and customize notification methods such as email, SMS, and in-app notifications.
+- **Input Processing**: Customize input processing settings for different input options, including text, file uploads, and real-time data streams.
+- **AI Model Selection**: Enable or disable specific AI models based on your requirements and configure model-specific settings such as confidence thresholds and analysis parameters.
+
+By leveraging the power of advanced AI models and providing a user-friendly interface, the Cybersecurity AI Platform aims to enhance your cybersecurity capabilities and help you stay ahead of emerging threats.

alerting.py

@@ -0,0 +1,70 @@
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+
+class AlertingSystem:
+    def __init__(self):
+        self.alert_thresholds = {
+            "phishing": 0.8,
+            "malware": 0.8,
+            "anomaly": 0.8
+        }
+        self.notification_methods = {
+            "email": self.send_email,
+            "sms": self.send_sms,
+            "in_app": self.send_in_app_notification
+        }
+
+    def set_alert_threshold(self, alert_type, threshold):
+        self.alert_thresholds[alert_type] = threshold
+
+    def get_alert_threshold(self, alert_type):
+        return self.alert_thresholds.get(alert_type, 0.8)
+
+    def send_alert(self, alert, method="email"):
+        notification_method = self.notification_methods.get(method)
+        if notification_method:
+            notification_method(alert)
+        else:
+            print(f"Notification method {method} not supported.")
+
+    def send_email(self, alert):
+        sender_email = "[email protected]"
+        receiver_email = "[email protected]"
+        subject = "Cybersecurity Alert"
+        body = f"Alert: {alert}"
+
+        msg = MIMEMultipart()
+        msg["From"] = sender_email
+        msg["To"] = receiver_email
+        msg["Subject"] = subject
+        msg.attach(MIMEText(body, "plain"))
+
+        try:
+            with smtplib.SMTP("smtp.example.com", 587) as server:
+                server.starttls()
+                server.login(sender_email, "your_password")
+                server.sendmail(sender_email, receiver_email, msg.as_string())
+            print("Email sent successfully.")
+        except Exception as e:
+            print(f"Failed to send email: {e}")
+
+    def send_sms(self, alert):
+        # Implement SMS sending logic here
+        print(f"SMS Alert: {alert}")
+
+    def send_in_app_notification(self, alert):
+        # Implement in-app notification logic here
+        print(f"In-app Notification: {alert}")
+
+    def monitor_real_time_data(self, data_stream, model_name, analyze_text):
+        for data in data_stream:
+            result = analyze_text(data, model_name)
+            if result["score"] >= self.get_alert_threshold(model_name):
+                self.send_alert(result)
+
+    def customize_notification_method(self, method, custom_function):
+        self.notification_methods[method] = custom_function
+
+    def get_notification_methods(self):
+        return list(self.notification_methods.keys())

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+from transformers import pipeline
+import requests
+import json
+import time
+import threading
+
+# Load AI models
+def load_models():
+    models = {
+        "gpt-3": pipeline("text-generation", model="gpt-3"),
+        "bert-base-uncased": pipeline("text-classification", model="bert-base-uncased"),
+        "roberta-large": pipeline("text-classification", model="roberta-large"),
+        "distilbert-base-uncased": pipeline("text-classification", model="distilbert-base-uncased"),
+        "albert-base-v2": pipeline("text-classification", model="albert-base-v2"),
+        "tinybert": pipeline("text-classification", model="tinybert"),
+        "cybersecurity-bert": pipeline("text-classification", model="cybersecurity-bert"),
+        "malware-detection-bert": pipeline("text-classification", model="malware-detection-bert"),
+        "phishing-detection-bert": pipeline("text-classification", model="phishing-detection-bert")
+    }
+    return models
+
+models = load_models()
+
+# Define functions to interact with AI models
+def analyze_text(text, model_name):
+    model = models.get(model_name)
+    if model:
+        return model(text)
+    else:
+        return "Model not found."
+
+def analyze_file(file, model_name):
+    content = file.read().decode("utf-8")
+    return analyze_text(content, model_name)
+
+# Real-time monitoring and alerting
+alert_thresholds = {
+    "phishing": 0.8,
+    "malware": 0.8,
+    "anomaly": 0.8
+}
+
+def monitor_real_time_data(data_stream, model_name):
+    for data in data_stream:
+        result = analyze_text(data, model_name)
+        if result["score"] >= alert_thresholds.get(model_name, 0.8):
+            send_alert(result)
+
+def send_alert(alert):
+    # Implement notification methods (e.g., email, SMS, in-app notifications)
+    print(f"Alert: {alert}")
+
+# Gradio interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Cybersecurity AI Platform")
+
+        with gr.Tab("Text Input"):
+            text_input = gr.Textbox(label="Enter text for analysis")
+            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
+            text_output = gr.Textbox(label="Analysis Result")
+            text_button = gr.Button("Analyze Text")
+            text_button.click(analyze_text, inputs=[text_input, model_dropdown], outputs=text_output)
+
+        with gr.Tab("File Upload"):
+            file_input = gr.File(label="Upload file for analysis")
+            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
+            file_output = gr.Textbox(label="Analysis Result")
+            file_button = gr.Button("Analyze File")
+            file_button.click(analyze_file, inputs=[file_input, model_dropdown], outputs=file_output)
+
+        with gr.Tab("Real-time Data Stream"):
+            data_stream_input = gr.Textbox(label="Enter real-time data stream URL")
+            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
+            real_time_output = gr.Textbox(label="Real-time Monitoring Result")
+            real_time_button = gr.Button("Start Monitoring")
+            real_time_button.click(monitor_real_time_data, inputs=[data_stream_input, model_dropdown], outputs=real_time_output)
+
+    demo.launch()
+
+if __name__ == "__main__":
+    gradio_interface()

customization.py

@@ -0,0 +1,47 @@
+class Customization:
+    def __init__(self):
+        self.alert_thresholds = {}
+        self.notification_methods = {}
+        self.custom_alert_rules = []
+        self.input_processing_settings = {}
+        self.model_settings = {}
+
+    def set_alert_threshold(self, alert_type, threshold):
+        self.alert_thresholds[alert_type] = threshold
+
+    def get_alert_threshold(self, alert_type):
+        return self.alert_thresholds.get(alert_type, 0.8)
+
+    def customize_notification_method(self, method, custom_function):
+        self.notification_methods[method] = custom_function
+
+    def get_notification_methods(self):
+        return list(self.notification_methods.keys())
+
+    def create_custom_alert_rule(self, rule):
+        self.custom_alert_rules.append(rule)
+
+    def get_custom_alert_rules(self):
+        return self.custom_alert_rules
+
+    def set_input_processing_setting(self, setting, value):
+        self.input_processing_settings[setting] = value
+
+    def get_input_processing_setting(self, setting):
+        return self.input_processing_settings.get(setting, None)
+
+    def enable_model(self, model_name):
+        self.model_settings[model_name] = {"enabled": True}
+
+    def disable_model(self, model_name):
+        self.model_settings[model_name] = {"enabled": False}
+
+    def set_model_setting(self, model_name, setting, value):
+        if model_name not in self.model_settings:
+            self.model_settings[model_name] = {}
+        self.model_settings[model_name][setting] = value
+
+    def get_model_setting(self, model_name, setting):
+        if model_name in self.model_settings:
+            return self.model_settings[model_name].get(setting, None)
+        return None

input_processing.py

@@ -0,0 +1,51 @@
+import os
+import json
+
+class InputProcessor:
+    def __init__(self):
+        self.settings = {
+            "text_processing": True,
+            "file_processing": True,
+            "real_time_processing": True,
+            "custom_settings": {}
+        }
+
+    def process_text(self, text):
+        if self.settings["text_processing"]:
+            # Implement text processing logic here
+            return text
+        else:
+            return "Text processing is disabled."
+
+    def process_file(self, file_path):
+        if self.settings["file_processing"]:
+            with open(file_path, 'r') as file:
+                content = file.read()
+                # Implement file processing logic here
+                return content
+        else:
+            return "File processing is disabled."
+
+    def process_real_time_data(self, data_stream):
+        if self.settings["real_time_processing"]:
+            # Implement real-time data processing logic here
+            return data_stream
+        else:
+            return "Real-time data processing is disabled."
+
+    def load_custom_settings(self, settings_file):
+        if os.path.exists(settings_file):
+            with open(settings_file, 'r') as file:
+                self.settings["custom_settings"] = json.load(file)
+        else:
+            return "Settings file not found."
+
+    def apply_custom_settings(self):
+        # Apply custom settings logic here
+        pass
+
+    def update_settings(self, new_settings):
+        self.settings.update(new_settings)
+
+    def get_settings(self):
+        return self.settings

models.py

@@ -0,0 +1,41 @@
+class AIModel:
+    def __init__(self, model_name, model):
+        self.model_name = model_name
+        self.model = model
+
+    def analyze(self, input_data):
+        return self.model(input_data)
+
+class ModelManager:
+    def __init__(self):
+        self.models = {}
+
+    def load_model(self, model_name, model):
+        self.models[model_name] = AIModel(model_name, model)
+
+    def update_model(self, model_name, model):
+        if model_name in self.models:
+            self.models[model_name].model = model
+
+    def enable_model(self, model_name):
+        if model_name in self.models:
+            self.models[model_name].enabled = True
+
+    def disable_model(self, model_name):
+        if model_name in self.models:
+            self.models[model_name].enabled = False
+
+    def analyze_with_model(self, model_name, input_data):
+        if model_name in self.models and self.models[model_name].enabled:
+            return self.models[model_name].analyze(input_data)
+        else:
+            return "Model not found or not enabled."
+
+    def get_model_names(self):
+        return list(self.models.keys())
+
+    def is_model_enabled(self, model_name):
+        if model_name in self.models:
+            return self.models[model_name].enabled
+        else:
+            return False

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+transformers
+torch
+pandas
+numpy
+scikit-learn
+requests
+flask

threat_intelligence.py

@@ -0,0 +1,72 @@
+import requests
+import json
+
+class ThreatIntelligence:
+    def __init__(self):
+        self.api_keys = {}
+        self.endpoints = {}
+        self.threat_data = []
+
+    def add_api_key(self, provider, api_key):
+        self.api_keys[provider] = api_key
+
+    def set_endpoint(self, provider, endpoint):
+        self.endpoints[provider] = endpoint
+
+    def fetch_threat_data(self, provider):
+        if provider in self.api_keys and provider in self.endpoints:
+            headers = {"Authorization": f"Bearer {self.api_keys[provider]}"}
+            response = requests.get(self.endpoints[provider], headers=headers)
+            if response.status_code == 200:
+                self.threat_data.append(response.json())
+            else:
+                print(f"Failed to fetch data from {provider}: {response.status_code}")
+        else:
+            print(f"API key or endpoint for {provider} not set.")
+
+    def aggregate_threat_data(self):
+        aggregated_data = {}
+        for data in self.threat_data:
+            for item in data:
+                threat_id = item.get("id")
+                if threat_id not in aggregated_data:
+                    aggregated_data[threat_id] = item
+                else:
+                    aggregated_data[threat_id].update(item)
+        return aggregated_data
+
+    def normalize_threat_data(self, data):
+        normalized_data = []
+        for item in data.values():
+            normalized_item = {
+                "id": item.get("id"),
+                "type": item.get("type"),
+                "description": item.get("description"),
+                "severity": item.get("severity"),
+                "source": item.get("source"),
+                "timestamp": item.get("timestamp")
+            }
+            normalized_data.append(normalized_item)
+        return normalized_data
+
+    def integrate_with_misp(self, misp_url, misp_key):
+        headers = {"Authorization": f"Bearer {misp_key}"}
+        response = requests.get(misp_url, headers=headers)
+        if response.status_code == 200:
+            self.threat_data.append(response.json())
+        else:
+            print(f"Failed to integrate with MISP: {response.status_code}")
+
+    def integrate_with_opencti(self, opencti_url, opencti_key):
+        headers = {"Authorization": f"Bearer {opencti_key}"}
+        response = requests.get(opencti_url, headers=headers)
+        if response.status_code == 200:
+            self.threat_data.append(response.json())
+        else:
+            print(f"Failed to integrate with OpenCTI: {response.status_code}")
+
+    def get_threat_data(self):
+        return self.threat_data
+
+    def clear_threat_data(self):
+        self.threat_data = []