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In recent years, blockchain (BC) technologies have been increasing for data secrecy, system reliability and safety. BC is vulnerable to cyberattacks despite its utility. According to the statistics, attacks are rare, which differs greatly from the average. The goal of BC attack detection is to discover insights, patterns and anomalies within massive data repositories, it may benefit from deep learning. In this paper, the Prevention of Insider Attacks using Blockchain with Hierarchical Auto-associative Polynomial Convolutional Neural Network in Cloud Platform (PIS-BCNN-CP) is proposed. Here, the node authentication is handled by the smart contract. The aim of authorizing a node is to confirm that only a particular node has the possibility to submit and recover the information. Then Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAAPCNN) is proposed to detect the Insider Attacks as Malicious and Normal. Generally, HAAPCNN does not agree with any optimization strategies to determine the optimal parameters for guaranteeing the exact detection of insider attacks. Hence, the Bear Smell Search Algorithm (BSSA) is exploited to optimize the weight parameters of a HAAPCNN. The BC Enabled Secure Data Storage depends on Proof of Continuous Work (PoCW) consensus BC algorithm is used. The proposed system is implemented and evaluated using performance metrics. The results provide higher accuracy, and lower False Negative Rate when compared with existing state-of-the-art methods.

The world is facing an era in which technology has a crucial impact on the growth of businesses. It is claimed that in 2020 20 billion devices will be connected to the internet, therefore, data is crucial for employees, customers and organisations. The implementation of the European General Data Protection Regulation (GDPR) gives the owner better control of their personal data. Consequently, organisations must be prepared to face cutting-edge threats to security in order to protect individuals against potential harm caused by unauthorized access to their data. Bring Your Own Device (BYOD) is a policy which provides some flexibility within organisations, and which results in better commitment in employees. This chapter analyses the impact of GDPR in BYOD architectures and proposes the integration of a multi-layer policy with an Information Governance Framework to ensure data privacy, focusing on the human factor when protecting personal devices.