-
-
A brief working of TrojaNix
Inspiration:
While we were exploring the inner workings of a malware framework , we stumbled upon various malware analysis tools such VirusTotal where in to test the legitimacy of the online website we tried to upload a C++ program file and it detected a trojan horse, we were stunned to find a malware in a C++ program file and that sparked the curiosity which led us here.
What it does:
It is a specialized program which has the ability to interpret all previous known malwares and has the ability to to detect incoming threats of all means and learn from previous malware experiences and safeguard against future mutations of the similar kind.
How we built it:
We went though all the known hashed malware signatures and the possible malware activity logs to train a deep learning model.
Challenges we ran into:
Obtaining the hashed signatures of the discreet malwares.
Selecting a framework and control structures for the model.
Creating a secure sandbox environment.
The coding and the effort of pre-planning.
The inaccessibility of proper real world datasets and experiences.
Accomplishments that we're proud of:
The fact that we could garner a plethora of knowledge in a fixed period if time to learn and apply it in real life.
What we learned:
Malwares and their functions.
Automation with python.
Log and network analysis.
Core Deep learning concepts.
Functioning and testing in virtual environments
What's next for TrojanNix:
We hope to gain the trust of SOC analysists across the world.
We hope to secure servers of major internet conglomerate.
Log in or sign up for Devpost to join the conversation.