Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology (AI-MLBBC): Anti-Crime-Theft Smart Wall Defense

Introduction

This chapter presents an integrated secured framework of blockchain and bitcoin cryptology with the artificial intelligence of neural networks and machine learning. Recently blockchain has been received special attention and used as a new platform for digital information and to store encrypted data and process secure digital transactions. Noticeably, the majority of blockchain cryptocurrency technology is structured based on the elliptic curves digital signature algorithm (ECDSA). In particular, Bitcoin uses special elliptic curves digital signature algorithm (ECDSA) called secp256k1. Losses of personal and organizational data have been reported and occurred due to security breaches of data at minor and major scales using traditional transactional and financial platforms. Furthermore, data on blockchain and Bitcoin platforms are assumed to be highly encrypted and secured state. This feature enable blockchain to be an ideal system to save confidential and personal data as well as sensitive organizational and financial information. Although blockchain and bitcoin databases are encrypted using private keys, but there are many cryptocurrency bitcoin wallets have been reported hacked which resulted in losing millions of dollars. Breaching blockchain can lead to exposing sensitive data to high risk. The artificial intelligent neural networks (AINN) algorithms possess the capability features of processing and operating encrypted data and will lead to minimal risk. Pretrained convolutional neural network (CNN) training is proposed to be implemented a part of the block chain to protect personal data and information as third wall defense against hacking. CCN is used to extract learned image features and use those features to train an image classifier with single pass making advantage of machine learning tools features.

In this chapter presents a literature survey is presented, mathematical background of elliptic curves for real and finite prime fields is introduced, elliptic curves cryptosystem is addressed, and bitcoin digital signatures are demonstrated. Moreover, a novel integrated cryptology approach of artificial intelligence based blockchain and bitcoins by introducing a multilayer security layer and neural networks machine learning and big data mining. Specifically, this revolutionary safe combination criteria is structured by implementing a coupled private key elliptic curves digital signature in order to securely enter into the blockchain and process encrypted data using neural networks. Furtherly, encrypted big data can be processed by neural networks linear regression and out-of-memory tall arrays criteria. Moreover, artificial intelligent machine learning of photography can be encrypted-decrypted by using a pertained Convolutional Neural Networks (CNN) and by utilizing Singular Value Decomposition (SVD) and XOR-Secret cipher key.

In this chapter, Section 1 will introduce a literature survey, Elliptic Curves Defined Over Real Field is presented in Section 2, Elliptic Curves Cryptosystem Over Finite Prime Fields is introduced in Section 3, Elliptic Curve Cryptographic Algorithm is addressed in section 4, moreover, Bitcoin Elliptic Digital Signature Cryptosystem is presented in Section 5, Multilayer Elliptic Curve Digital Signatures are introduced in Section 6. Moreover, Protection of Data Using Pretrained Convolutional Neural Network (CNN) and Singular Value Decomposition (SVD) is proposed in Section 7 along with simulations results. Finally, conclusions are drawn in Section 8.