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Big data characteristics (V’s) in industry
Get PDFIn the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from
Development and validation of the male rape myth acceptance scale (MRMAS)
Get PDFDespite growing recognition of male-on-male rape and its related myths, research in this area has been held back by the lack of a reliable and comprehensive measure or scale. The present work utilises a large and diverse participant sample over two studies (Study 1 N = 510, Study 2 N = 527) to validate a new Male Rape Myth Acceptance Scale (MRMAS), measuring myths falling under six principle themes: masculinity, sexuality, pleasure, perpetrators, context, and effect. Analysis suggested a two-factor scale, with ‘Blame’ and ‘Minimisation/Exoneration’ sub-scales. Both the overall scale and sub-scales demonstrate excellent reliability and construct validity, and are thus proposed as tools to enable the proliferation of future research on male rape myth acceptance, both in general and specialist populations, in an attempt to improve the experiences of male rape victims
Efficient resource allocation using distributed edge computing in D2D based 5G-HCN with network slicing
Get PDFFifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead
Applied cryptography in network systems security for cyberattack prevention
Get PDFApplication of cryptography and how various encryption algorithms methods are used to encrypt and decrypt data that traverse the network is relevant in securing information flows. Implementing cryptography in a secure network environment requires the application of secret keys, public keys, and hash functions to ensure data confidentiality, integrity, authentication, and non-repudiation. However, providing secure communications to prevent interception, interruption, modification, and fabrication on network systems has been challenging. Cyberattacks are deploying various methods and techniques to break into network systems to exploit digital signatures, VPNs, and others. Thus, it has become imperative to consider applying techniques to provide secure and trustworthy communication and computing using cryptography methods. The paper explores applied cryptography concepts in information and network systems security to prevent cyberattacks and improve secure communications. The contribution of the paper is threefold: First, we consider the various cyberattacks on the different cryptography algorithms in symmetric, asymmetric, and hashing functions. Secondly, we apply the various RSA methods on a network system environment to determine how the cyberattack could intercept, interrupt, modify, and fabricate information. Finally, we discuss the secure implementations methods and recommendations to improve security controls. Our results show that we could apply cryptography methods to identify vulnerabilities in the RSA algorithm in secure computing and communications networks
Cyberattack ontology: a knowledge representation for cyber supply chain security
Get PDFCyberattacks on cyber supply chain (CSC) systems and the cascading impacts have brought many challenges and different threat levels with unpredictable consequences. The embedded networks nodes have various loopholes that could be exploited by the threat actors leading to various attacks, risks, and the threat of cascading attacks on the various systems. Key factors such as lack of common ontology vocabulary and semantic interoperability of cyberattack information, inadequate conceptualized ontology learning and hierarchical approach to representing the relationships in the CSC security domain has led to explicit knowledge representation. This paper explores cyberattack ontology learning to describe security concepts, properties and the relationships required to model security goal. Cyberattack ontology provides a semantic mapping between different organizational and vendor security goals has been inherently challenging. The contributions of this paper are threefold. First, we consider CSC security modelling such as goal, actor, attack, TTP, and requirements using semantic rules for logical representation. Secondly, we model a cyberattack ontology for semantic mapping and knowledge representation. Finally, we discuss concepts for threat intelligence and knowledge reuse. The results show that the cyberattack ontology concepts could be used to improve CSC security
Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling
Get PDFNowadays, energy crisis is considered an essential active issue for future urbanization in megacities. While the
rate of population growth increases, the volume of municipal solid waste production increases significantly. This
highlights the need of Sustainable Development Goals (SDGs) for both developed and developing countries. This
paper constructs a novel smart framework for supplying biogas energy. Our study is applicable for fields of waste
management and energy supply in green buildings. The proposed framework integrates the Response Surface
Methodology (RSM), Artificial Intelligence (AI), and Petri net modeling. In this regard, the AI techniques
including the Random Tree (RT), Random Forest (RF), Artificial Neural Network (ANN) and, Adaptive-Networkbased
Fuzzy Inference System (ANFIS) are employed. In addition, for creating the optimum condition, a dynamic
control system using the Petri Net modeling is applied. Among all machine learning methods, ANFIS with 0.99
correlation coefficient had the best accuracy for Accumulated Biogas Production (ABP) based on effective factors.
Finally, the main findings of this paper are to introduce a novel framework for addressing different scientific
issues such as supplying the clean energy in green buildings, the development of a smart and sustainable biogas
production control system, integration of solid waste management with the SDGs in green buildings
Ethics, rationing and the COVID-19 pandemic: philosophy and practice
Get PDFTwo approaches to bioethical and broader applied philosophical debate are discussed and their implications in the context of the current Covid discourse are examined. It is argued that an approach designed to be more 'practical' can be counter-productive, and a more traditional approach to critical thinking has a new and vital role in the context of our current moral and epistemic controversies
A critical analysis of volatility surprise in Bitcoin cryptocurrency and other financial assets
Get PDFThe purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use the sec-ondary data, i.e., price history of Bitcoin from September 2014 to September 2021 for the last 7 years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of S&P 500, gold, and Treasury Bond. Our findings show that Bitcoin has very high volatility as compared to S&P 500, Gold and Treasury Bond. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and other three financial assets before and during COVID-19. Hence Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with the understanding of how the future of Bitcoin is likely to span out. This has important im-plications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on the long-term social sustainability vis a vis supply chain traceability