BP Code Calc Exe 🔽

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BP Code Calc Exe

the first part of this tutorial includes an example of what a public key is, and how a public/private key pair is used. it’s one of the simplest examples of public/private key encryption, but there are so many details that you’ll want to understand to read the rest of the tutorial. much of the rest of the tutorial comes from other sources, but it’s a good place to start. those details are outlined in the in this section, and elsewhere.

the main objective of the challenge was to create a stub programme that would have no existing functionality, other than launching a shellcode downloader. the downloader would then download a zip file that when opened, would attempt to crack a password if one had been supplied. however, the solution does a little more than this. after all the decryption, it then calls a known routine to set the flag and then exit. this causes the target exe to run normally, except that it will have a flag set. the remainder of this writeup will walk you through the solution with explanations and analysis.

it is important to note that i discovered that i could search for a function string in the.net framework if the program was already running rather than from a debugger. this is why you can see that the code locations are green in windbg.

let’s start by creating a simple shellcode downloader and password cracker, in this case written in c#. for simplicity, it will have no functionality other than downloading a zip file from a url, extracting the contents into the current directory (you can use curl on linux), and then trying to crack a hard-coded password and set the flag.

You’re welcome! Sorry it’s so late. I tryed to look in to tensorflow and it turned out that you need to do the reshape on the x_images and y_outputs tensors. Then you can call the train procedure again. Sorry I can’t give you a code for that right now, but that should be a good starting point.
Hi, Thanks for this tutorial! It was great to see how a neural network works. I’ve looked through your code and understand what is going on. It’s very useful. I just wanted to point out that I’m assuming that y_pred is of the same shape as y_actual, i.e. 1-d. This is crucial because otherwise the error (gradient) are going to be multi-dimensional which can get very confusing. It would be good to add a note to your code in the function that n_samples might not be a scalar. I’m not too concerned with the loop taking a few seconds to execute because I’m just using it to calculate the cost to see if the training is converging. The code computes an error as I expected and then the train step is repeated until the next iteration. Your code is great and I definitely recommend your tutorial. Thanks!
Hi Xuan: I dont know whether I am just missing it or not. But I tried your code to implement a hidden layer and works perfectly. Do I need to assign output of hidden layer to a neuron and use some other activation function? It didnt work if I directly used softmax. I know there are already softmax implemented in scipy, but that is a loss function. I dont want to use the loss function. I want to use the error backpropagation. So I have to give a different activation function to the output neuron. For example, I want to have a neuron output a value between 0 and 1, so it can be identified as either a 0 or a 1. I think I did that right. I modified the test file to use backpropagation and it worked well. I dont know what I can do more. Can you offer any suggestion? Thanks. Happy coding!
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M. Kamal CPA P.C.
70-17 37th Avenue Suite #2F, Jackson Heights, NY, 11372

M. Kamal CPA P.C.

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BP Code Calc Exe 🔽

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