How to make predictions on files about malware-detection-using-machine-learning HOT 5 CLOSED

I am in seek for support to my academic project. May I have access to your project documentation please!

from malware-detection-using-machine-learning.

from malware-detection-using-machine-learning.

Thank you for your response. I have already found out solutions to my problems for now. If I will have problem in future then I will be remembering you again.

from malware-detection-using-machine-learning.

First of all thank you very much for supplying all this as its very difficult to follow the documentation on the kaggle challenge. I have the n_final_hybrid_valid.csv and optimize folder with checkpoints. Im using windows 10 x64 and python3.6 and the folders it creates wont open saying there corrupted or damaged. I have also tried saving them as rar files but same thing happens. My main question here is what data must I pass to the ann_hybrid to make a prediction on a unknown file? is that the proper way to use this or does a prediction module need to be written?. Would i need to write something that will take the file path as a input then disassemble it and extract the byte code and normalize the data from that file to match the output in the CSV? please help and thank you for your time.

I hope to hear from you soon!
GREAT WORK!!

Hi,

Thanks for the appreciation. This work is only for academic purposes right now. Yes, you may need to write predict module for new samples, but you have to follow the same workflow as mentioned in the paper and in doing so, you need to select only that features of ASM as mentioned in the regarding CSV. Please let me know if you face any problem while running this code.

Thanks!

from malware-detection-using-machine-learning.

I am in seek for support to my academic project. May I have access to your project documentation please!

Hi,

Thanks for asking. Please find the preprint version of the paper on arxiv.org (https://arxiv.org/abs/1910.10958). It almost covers every aspect of the project.

Thanks

from malware-detection-using-machine-learning.