SK. TANZIR MEHEDI

The blockchain is nothing but a skilled magician one after one it is providing the mankind with wonders in the era of information technology and financial industry. In recent years blockchain has received tremendous attention to providing a distributed, definitive and auditable applications in the field of Internet of Things (IoT). The predominant Internet of Things (IoT) is moving towards momentous scalability and security challenges. Blockchain technology is extravagant and entangles high bandwidth, prolongation and memory overhead that are not compatible with IoT devices. This paper brings forward a new definitive, intimate as well as lightweight masonry for IoT based blockchain technology which forsakes the memory overhead and centralized system while security and privacy benefits are maintaining. The preliminary investigation method is discussed a standardized IoT infrastructure; where data is stored and access is managed by a decentralized blockchain technology. The following integration method used terminal devices as network technology and Ethereum as the blockchain platform which produced such kind of backend that ensure high availability, security, and privacy while replacing traditional backend systems. Diametrically, we illustrate the simulation outcome to highlight our approach that significantly related to security and privacy of Blockchain-based IoT applications.

The blockchain is a marvelous invention in the era of information technology. In recent years blockchain technology has given amazing attention as a way to deliver a distributed, secure, and auditable of smart devices. Current products selection or buying process involves a good number of specifications. It is not uncommon for any fraud company to misrepresent, overstate, specifically various functions. Where this occurs and the buyer is subsequently buying those products, then it may be exposed to significant commercial and legal risk. To verify the authenticity of a product buyer usually depends on the local seller. Because of the conventional process of products verification is time-consuming and costly. Moreover, the buyer has to rely on third parties, which may not be trustworthy. Therefore, general people usually avoid the verification process of products. In this research, we demonstrate how blockchain technology can provide cost-effective, and real-time manufacture history verification. The proposed approach also ensures trustworthy and privacy-preserving manufacture-history of data sharing. Furthermore, to explain how sellers can share and buyers can verify their buying product we have implemented a prototype to record manufacture history using Ethereum-based public blockchain.

Photonic crystal fibers (PCFs) may be considered an affiliate of a most ordinary class of microstructured optical fibers (MOFs) with a repeated layout of different refractive index material. A D-shape PCF is introduced in this paper to achieve high nonlinearity (γ), low confinement loss (Lc), low scattering loss (αR), and high numerical aperture (NA) by selecting finite element method (FEM) for 100 nm – 700 nm wavelength and physics-controlled triangular extra fine mesh which size of elements is 5174 by comprehensive simulation software COMSOL Multiphysics. From the structure, an outstanding performance profile is created which contains high nonlinearity of 6.01 × 1013W−1Km−1, low scattering loss of 1.01 × 10−5 [dB m−1], low confinement loss of 1.39 × 10−11 [dB m−1], and high numerical aperture of 0.87 where the effective mode area of the core is 1.15 × 10−14m2 at the functional wavelength of 150 nm. To exhibit this performance profile, in this simulation, silica is used as background material and graphene as core material. The performance profile is highly preferable for optical communication in biomedical imaging, and other nonlinear applications. Hence, D-shape photonic crystal fiber (D-PCF) is a better alternative to other optical fibers for these applications.

In order to predict the value of a target variable in a dataset, regression algorithms are used. There are quite a few number of regression algorithms and their performances differ. In this work, we have created a dataset of central processing unit (CPU) components and we predict the performance of the CPUs using five different kinds of regression algorithm, namely, linear regression, gradient boosting regression, support vector machine regression, decision tree regression and random forest regression and compare the coefficient of determination (R2) score and different kinds of errors, such as, mean absolute error, mean squared error and root mean squared error. The result have differed for each regression algorithm and we present a comparative analysis.

IoT is a revolution of a new era. In all of this applications data transfer from machine to machine, machine to human. Between this transection of data should ensure security. This technology makes human life easy but security is the main challenge. Compromising of security is the great threat of human life. Security is widely interconnected to the Internet of Things technology. Lots of researchers search for solutions to these threats and provide lots of techniques to provide security. All of these techniques should need a proper arrangement. The application layer is the last layer and the only one that directly interacts with the end-user. To be secured all the user and service providers should aware of these threats and security issues. In this layer, the user uses and get services from the service provider. To give proper security different approaches have been taken to give security to have secured services. The paper helps future researchers to have a comparative analysis of the application layer protocol’s performance. Furthermore, the paper discusses the architecture implementations, security provisions of certain recognized application layer protocols nowadays being used in an IoT technology also discusses about traditional solutions.

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.