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.

In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality. This article aims to overcome these limitations by proposing an efficient transformer-based multilevel attention (ETMA) framework for multimodal fake news detection, which comprises the following components: a visual attention-based encoder, a textual attention-based encoder, and joint attention-based learning. Each component utilizes different forms of attention mechanisms and uniquely deals with multimodal data to detect fraudulent content. The efficacy of the proposed network is validated by conducting several experiments on four real-world fake news datasets: Twitter, Jruvika fake news dataset, Pontes fake news dataset, and Risdal fake news dataset using multiple evaluation metrics. The results show that the proposed method outperforms the baseline methods on all four datasets. Furthermore, the computation time of the model is also lower than the state-of-the-art methods.

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.

Quantum computers use quantum-mechanical phenomena for knowledge manipulation and depend on quantum bits or qubits. A qubit can be created in several different ways, and out of this, one way of creating a quantum state is by using superconductivity. They must be held very cold to work on these superconductive qubits for long periods. The key to information storage and manipulation is the skill of all computer systems. Current traditional computers handle single bits stored in binary states of 0 and 1 form. Every temperature factor inside the device may be updated; thus, quantum computers are more excellent than the vacuum of space at temperatures similar to absolute null. Consider how the dilution refrigerator of a quantum computer consisting of over 2000 components provides a cold atmosphere for the inside of the qubits. Researchers from all around the world today are using actual quantum processors for validating algorithms for different fields of operation. Yet quantum computation was a strictly speculative topic a couple of decades ago. Quantum cryptography, also known as quantum encoding, uses quantum mechanics principles to encrypt messages in a way nobody else reads. It benefits quantum states, along with its "theory of no transition," which means that it cannot be disrupted unknowingly. Quantum-improved AI calculations are especially applicable to the area. This work focuses on the implementation patterns of quantum computing in real-time cryptography so that the overall communication will be secured and integrity aware.