dtrizna

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...

dtrizna

#!/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):

dtrizna

Kerberos cheatsheet

Bruteforcing

With kerbrute.py:

python kerbrute.py -domain <domain_name> -users <users_file> -passwords <passwords_file> -outputfile <output_file>

With Rubeus version with brute module:

dtrizna

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)

dtrizna

using System;
using System.Reflection;
using Microsoft.CSharp;
using System.Diagnostics;
using System.Runtime.InteropServices;

public class Program
{
    public static void Main()
    {