This script performs sentiment analysis on the email contents using the sentiment analysis model and applies a pre-trained phishing detection model to identify potential phishing emails. After connecting to Exchange Online and retrieving the emails, the Python code loads the sentiment analysis model and the phishing detection model. It then processes each email’s contents, performs sentiment analysis, and computes a phishing score using the phishing detection model. If the phishing score exceeds a threshold (in this case, 0.5), the email is considered a potential phishing attempt and is added to the phishing_emails list. The script outputs information about the detected phishing emails, including the subject, sender, sentiment analysis result, and phishing score. If no phishing emails are detected, it provides an appropriate message.

# Install required PowerShell modules if not already installed
if (-not (Get-Module -ListAvailable -Name ExchangeOnlineManagement)) {
    Install-Module -Name ExchangeOnlineManagement -Force -AllowClobber
}

# Import the required modules
Import-Module ExchangeOnlineManagement

# Connect to Exchange Online
Connect-ExchangeOnline -Credential (Get-Credential)

# Retrieve a list of emails
$emails = Get-EXOMailboxMessage -ResultSize 100

# Import the required PyTorch modules (assuming you have Python and PyTorch installed)
$pythonCode = @"
import torch
from transformers import pipeline

# Load the sentiment analysis model
sentiment_model = pipeline('sentiment-analysis')

# Load the phishing detection model
phishing_model = torch.hub.load('path_to_phishing_model', 'phishing_detection')

phishing_emails = []

# Perform sentiment analysis and phishing detection on emails
for email in emails:
    # Process email contents (e.g., cleaning, tokenization)

    # Perform sentiment analysis on email contents
    sentiment_result = sentiment_model(email['Body'])

    # Perform phishing detection using the model
    phishing_score = phishing_model(email['Body'])

    # Check if the email is potentially a phishing attempt
    if phishing_score > 0.5:
        phishing_emails.append(email)

# Output the phishing detection results
if phishing_emails.Count -gt 0 {
    Write-Output "Phishing emails detected:"
    foreach ($phishing_email in $phishing_emails) {
        Write-Output "Subject: $($phishing_email.Subject)"
        Write-Output "Sender: $($phishing_email.From.Name) <$($phishing_email.From.Address)>"
        Write-Output "Sentiment: $($sentiment_model($phishing_email.Body)[0]['label'])"
        Write-Output "Phishing Score: $phishing_score"
        Write-Output "------------------------"
    }
} else {
    Write-Output "No phishing emails detected."
}
"@

# Run the Python code using the Python executable
$output = & python -c $pythonCode

# Output the phishing detection results
Write-Output $output

# Disconnect from Exchange Online
Disconnect-ExchangeOnline