Automated and industrial Internet of Things (IoT) devices are increasing daily. As the number of IoT devices grows, the volume of data generated by them will also grow. Managing these rapidly expanding IoT devices and enormous data efficiently to be
available to all authorized users without compromising its integrity will become essential in the near future. On the other side, many information security incidents have been recorded, increasing the requirement for countermeasures. While safeguards against hostile third parties have been commonplace until now, operators and parties have seen an increase in demand for data falsification detection and blocking. Blockchain technology is well-known for its privacy, immutability, and decentralized nature. Single-board computers are becoming more powerful while also becoming more affordable as IoT platforms. These single-board computers are gaining traction in the automation industry. This study focuses on a paradigm of IoT-Blockchain integration where the blockchain node runs autonomously on the IoT platform itself. It enables the system to conduct machine-to-machine transactions without the intervention of a person and to exert direct access control over IoT devices. This paper assumed that the readers are familiar with Hyperledger Fabric basic operations and focus on the practical approach of integration. A basic introduction is provided for the newbie on the blockchain.
Data Loss/Leakage Prevention (DLP) continues to be the main issue for many large organizations. There are multiple numbers of emerging security attach scenarios and a limitless number of overcoming solutions. Todays enterprises major concern is to pr
otect confidential information because a leakage that compromises confidential data means that sensitive information is in competitors hands. Different data types need to be protected. However, our research is focused only on data in motion (DIM) i-e data transferred through the network. The research and scenarios in this paper demonstrate a recent survey on information and data leakage incidents, which reveals its importance and also proposed a model solution that will offer the combination of previous methodologies with a new way of pattern matching by advanced content checker based on the use of machine learning to protect data within an organization and then take actions accordingly. This paper also proposed a DLP deployment design on the gateway level that shows how data is moving through intermediate channels before reaching the final destination using the squid proxy server and ICAP server.
Biological data mainly comprises of Deoxyribonucleic acid (DNA) and protein sequences. These are the biomolecules which are present in all cells of human beings. Due to the self-replicating property of DNA, it is a key constitute of genetic material
that exist in all breathingcreatures. This biomolecule (DNA) comprehends the genetic material obligatory for the operational and expansion of all personified lives. To save DNA data of single person we require 10CD-ROMs.Moreover, this size is increasing constantly, and more and more sequences are adding in the public databases. This abundant increase in the sequence data arise challenges in the precise information extraction from this data. Since many data analyzing and visualization tools do not support processing of this huge amount of data. To reduce the size of DNA and protein sequence, many scientists introduced various types of sequence compression algorithms such as compress or gzip, Context Tree Weighting (CTW), Lampel Ziv Welch (LZW), arithmetic coding, run-length encoding and substitution method etc. These techniques have sufficiently contributed to minimizing the volume of the biological datasets. On the other hand, traditional compression techniques are also not much suitable for the compression of these types of sequential data. In this paper, we have explored diverse types of techniques for compression of large amounts of DNA Sequence Data. In this paper, the analysis of techniques reveals that efficient techniques not only reduce the size of the sequence but also avoid any information loss. The review of existing studies also shows that compression of a DNA sequence is significant for understanding the critical characteristics of DNA data in addition to improving storage efficiency and data transmission. In addition, the compression of the protein sequence is a challenge for the research community. The major parameters for evaluation of these compression algorithms include compression ratio, running time complexity etc.
Food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges H
yperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market.
