With kerbrute.py:
python kerbrute.py -domain <domain_name> -users <users_file> -passwords <passwords_file> -outputfile <output_file>With Rubeus version with brute module:
| model.zero_grad() # Reset gradients tensors | |
| for i, (inputs, labels) in enumerate(training_set): | |
| predictions = model(inputs) # Forward pass | |
| loss = loss_function(predictions, labels) # Compute loss function | |
| loss = loss / accumulation_steps # Normalize our loss (if averaged) | |
| loss.backward() # Backward pass | |
| if (i+1) % accumulation_steps == 0: # Wait for several backward steps | |
| optimizer.step() # Now we can do an optimizer step | |
| model.zero_grad() # Reset gradients tensors | |
| if (i+1) % evaluation_steps == 0: # Evaluate the model when we... |
| #!/usr/bin/env python3 | |
| """ | |
| Very simple HTTP server in python for logging requests | |
| Usage:: | |
| ./server.py [<port>] | |
| """ | |
| from http.server import BaseHTTPRequestHandler, HTTPServer | |
| import logging | |
| class S(BaseHTTPRequestHandler): |
With kerbrute.py:
python kerbrute.py -domain <domain_name> -users <users_file> -passwords <passwords_file> -outputfile <output_file>With Rubeus version with brute module:
| using System; | |
| using System.Reflection; | |
| using Microsoft.CSharp; | |
| using System.Diagnostics; | |
| using System.Collections.Generic; | |
| using System.Runtime.InteropServices; | |
| public class Program | |
| { | |
| static void Main(string[] args) |
| using System; | |
| using System.Reflection; | |
| using Microsoft.CSharp; | |
| using System.Diagnostics; | |
| using System.Runtime.InteropServices; | |
| public class Program | |
| { | |
| public static void Main() | |
| { |