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.Runtime.InteropServices; | |
public class Program | |
{ | |
public static void Main() | |
{ |
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) |
With kerbrute.py:
python kerbrute.py -domain <domain_name> -users <users_file> -passwords <passwords_file> -outputfile <output_file>
With Rubeus version with brute module:
#!/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): |
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... |