Analysis of Blockchain Protocol Using Machine Learning for Lightweight Cryptography
Keywords:
Blockchain Technology , Lightweight Cryptography ,Resource-Constrained Environments ,Internet of Things (IoT) , Decentralized Security , Computational OverheadAbstract
Anonymity, security, immutability, and audibility are four of blockchain's important properties that have garnered a lot of attention recently. The Internet of Things is only one of many non-monetary uses for blockchain technology. While present-day blockchain technology works well under more ideal conditions, it runs into problems when used in places with limited resources. In such limited contexts, conventional cryptographic techniques, developed for stronger systems, can impose a heavy computational burden. This limits the efficacy of blockchain applications by impeding their scalability and performance. The problem with the current approach is that it uses very computationally intensive cryptographic processes, which might be a bottleneck for devices with less power and memory. By offering cryptographic methods tailored to reduce computing cost, lightweight cryptography provides a more efficient option. Our suggested solution focuses on incorporating lightweight cryptography into blockchain in order to circumvent this obstacle. To drastically cut down on the blockchain system's resource consumption, our suggested approach makes use of lightweight cryptographic primitives. This allows the system to be deployed and operated successfully even in contexts with limited resources, while also improving its overall efficiency. Blockchain technology offers public digital ledgers and decentralised security, however it isn't ideal for devices with limited resources because of its high energy consumption, processing overhead, and significant delays.
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