In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

The accomplishment of blockchain has increased the focus on the various applications for simplifying the confidentiality and transaction sanctuary using the decentralized architecture via consensus mechanisms between different internet of things (IoT) nodes in daily increasing societal areas. The growth of blockchain lasted to grow and used to do compare technologies. The major shortcomings of blockchain is the lack of scalability in modern application settings. Holochain technology vends itself as a “thinking” exterior to blocks, and it is a peer-to-peer disseminated ledger technology. It works contrarily compared to the blockchain, and it offers an exclusive value in the existing market. IoT devices are continuously used in distributed environments, in various smart applications. The peer-to-peer IoT networks, connected to smart agricultural systems are exposed to the security issues. Specifically, the personal data of agricultural land records need protection against unauthorized access and eradicate corruption in land transactions. The Blockchain offers a possible solution based on distributed ledger, but it has scalability issues due to high storage and processing requirements with growing network size. Also data is not locally stored in a Blockchain. This paper studies the conventions of holochain technology, its architecture and challenges, and critical mechanisms of holochain applications. We also analyze the numerous models utilized for the implementation of protected transactions. We discuss an agent centric framework with distributed hash table for secured applications.