We help classify illegal and unsafe bitcoin transactions based on Bitcoin address
We help you identify safe (white) bitcoin transactions based on Bitcoin address
Inspiration With the rise in cryptocurrencies such as Bitcoin, there has been a tremendous surge in ransomware attacks owing to the complexities of the system and the anonymity enjoyed by the illegal entities. Existing heuristic mechanisms in place to detect such malicious transactions suffer from several fallacies and have failed to deploy advanced techniques for detection purposes. Ransomware is a malicious software that takes control of one’s computer, affects it and releases the system upon securing a ransom payment. There are several reasons which make Bitcoin a hotspot center for illegal transactions. Bitcoin continues to remain the only virtual currency which has a widespread user-base and is convertible in nature. Bitcoin transactions can be carried out anonymously by cybercriminals as it does not require identity verification and only requires them to send a public Bitcoin address via anonymous networks. The entirety of process from wallet creation, to accepting the payment and laundering it is fully automated and is additionally incontrovertible, meaning payment once done can’t be charged back. Furthermore, usage of anonymous networks such as Tor and non-traceable payment networks make it extremely difficult for law enforcement agencies to track down such ransom websites and shut them down. FBI agent Joel DeCapua has suggested that more than $144 million have been paid as Bitcoin ransom payments between 2013 and 2019 and similar figures have been estimated by a Google/Princeton study.
What it does The aim of the proposed model is to leverage machine learning techniques such as supervised learning to identify malicious ransomware Bitcoin transactions based on highly influential features of the used Bitcoin addresses that display high utility in detecting a ransom transaction.
How I built it Using Jupyter Notebook
Challenges I ran into A Lagging PC, lack of coffee to keep me awake the whole night ! and several other challenges
Accomplishments that I'm proud of This will be now the world's first-ever model for such a task !
What I learned Advanced ML techniques, Time Management
What's next for MoneyHeist 2.0 Making it available to my fellow netizens ! Yes