Blockchain Anomaly Detection with Machine Learning and Deep Learning Algorithms
Being a DLTian (which is what we who work at DLT Labs™ call ourselves), blockchain is a prime thing that we need to take care of in the products for data storage. All of this can be interpreted in terms of transactions. Blockchain relies on its hashing chain technology for maintaining the integrity of its transactions.
Any tampering can be caught as tampering a record will require tampering with the hashes of the entire chain as the chain will break as the previous block in the chain stores the hash of the current block which forms a chain.
>> There are two important aspects of security handled by blockchain:
- It ensures that the secure transactions completed by a participant cannot be tampered with; neither en route while being added to the chain nor after being added.
- It also ensures that every transaction allowed to be written conforms to the rules predefined by blockchain that are either programmed into the platform or added as smart contracts.
We rely on the security of blockchain for the above reasons. But there is another aspect of security that is also important i.e., reducing the possibility of fraudulent transactions occurring by enhancing its fraud detection.
As blockchain’s primary function is making secured transactions between participants, it becomes extremely important to strengthen anomaly detection systems to preserve its essence and effectiveness.
There may be a handful of such technologies that contribute to making blockchain-based transactions more secure. But let me share one such technology which shows a lot of promise in this regard. Allow me to introduce you to machine learning and deep learning.
Machine/Deep Learning Spam Detection
In our last post about Machine Learning and Deep Learning, we talked about some of its valuable contributions and use cases (if you have not read it, I would recommend you read that first).
One specific use case that we covered, and also one I want to bring to light again is the ‘Email Spam Classification’. Let us understand this use case in…
