Big Data Cyber Security Analytics

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Navigating Social Media in #Ophthalmology

Today, the implementation of measures for cybersecurity of information and communication systems and critical infrastructure of the state, countering and responding to computer incidents and cyber influences - remains one of the major tasks in the field of information security and cybersecurity of the state. Defining the directions of Ukraine᾿s foreign policy in the field of cybersecurity, the Strategy establishes that Ukraine will intensify its participation and partnership in international processes of standardization and certification in the field of cybersecurity, increase its “presence” in international, regional and other standardization bodies and institutes and certification of this area in the geopolitical sense. Based on the need for scientific substantiation of the institutional framework for the development of cybersecurity, special attention should be paid to the development of standards in the field of new IT technologies (including artificial intelligence, cloud databases, quantum computing and communications) and basic Internet architecture. The review of the problem field of research shows that in general the Internet should be comprehensive and open, the technologies supported and used in it should be focused on the person, his basic freedoms, guarantee neutrality to his privacy, ensure his privacy in cyberspace, and any restrictions should be carried out only in accordance with laws and regulations (standards). Ukraine᾿s position is that the use of certain technological processes should be legal and safe, in accordance with existing ethical standards. Current laws and regulations of the state, current national standards, including those developed and implemented, have been formulated taking into account promising areas of development of mechanisms for institutional support of cyber security and based on Euro-Atlantic experience. This allowed them to be implemented quite effectively in the regulatory framework of Ukraine. Nevertheless, during the analysis and generalization of theoretical approaches to the essence and content of some definitions, experts came to the appropriate legal conflict, namely that national standards contain requirements in partially similar areas of Ukrainian society, only with other names, such as: “information security”, “information technology security”, and “cybersecurity”. Thus, the author considers in detail the theoretical and practical significance of the draft Law of Ukraine “On Amendments to Some Laws of Ukraine on Standardization in the Areas of Cryptographic and Technical Protection of Information, Cyber Security, Counteraction to Technical Intelligence” at the initiative of MPs Fedienko O.P., Klochko A. and others, registration number 6568 of “28” January 2022 (first reading). Therefore, based on the implemented Cyber Security Strategy, which entered into force on 28.08.2021 and it is obvious that in the process of achieving its goals requires a systematic approach, the issue of legislation and standardization in cyber security and defining a specific subject of standardization in this area is extremely important.

Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance levels of data-driven algorithms even in the presence of large data volume, velocity, and variety (3Vs). BDA software complexity frequently leads to delayed deployments, longer development cycles, and challenging performance assessment. This paper proposes a Domain-Specific Model (DSM), and DevOps practices to design, deploy, and monitor performance metrics in BDA applications. Our proposal includes a design process, and a framework to define architectural inputs, software components, and deployment strategies through integrated high-level abstractions to enable QS monitoring. We evaluate our approach with four use cases from different domains to demonstrate a high level of generalization. Our results show a shorter deployment and monitoring times, and a higher gain factor per iteration compared to similar approaches.

Purpose of the study: This study aimed to explore cyber-security and cyber threats in social media. It includes an analysis of how social media is used as a tool for cyberattacks, the kinds of cyberattacks, and what people are doing to prevent them.
 Methodology: The paper was prepared using the critical literature review method, mainly in cybersecurity and cyber threats in social media.
 Main findings: Cybersecurity experts have warned that social media sites like Facebook, Twitter, and Instagram are particularly vulnerable to cyber-attacks because they are used extensively by companies and governments to distribute propaganda, to launch cyberattacks and are full of personal information (like passwords) that can be valuable to criminals.
 Application of the study: The presented article refers to cybersecurity and cyber threats in social media. It implies reflections in such scientific fields as, among others, security sciences.
 The study's originality: The study identifies cyber security measures used to handle the identified threats.

Chapter 16 - Big Data Analytics and Social Media in Disaster Management

Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

Spatial Patterns of Purposeful Social Networking and Socioeconomic Influences: Towards a Mature, Inclusive Networked Society in the U.S.

Big data analytics (BDA) methodologies and tools are used extensively for retailing at China. However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise BDA application areas in the retail at China comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose a systematic review methodology for the field of BDA in retailing. Second, we use the methodology proposed for a systematic literature review on BDA techniques in the retailing fields aiming at classifying the existing BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of BDA in retailing at China.

In the field of information and cybersecurity, one of the most important and critical challenges is the human factor, because no software or technical tool can fully compensate for the lack of awareness of information and cyber risks, appropriate behavior and a responsible attitude to information protection. The introduction of cognitive science theories into the field of cyber security will increase the level of effectiveness of protection strategies. Cognitive modeling contributes to the creation of mathematical models that simulate the processes of human thinking, decision-making and behavior, which brings the transition from reactive protection to a proactive approach. This article is devoted to the study of the implementation of the cognitive approach in security systems. Based on the analysis of scientific literature, the main definitions of cognitive science are highlighted, in particular, the concepts of cognitive modeling, cognitive analysis and synthesis, types of cognitive models, fuzzy cognitive map. The advantages of cognitive theories in various sectors of society are outlined. It has been proven that cognitive modeling can be applied in the field of cybersecurity to understand and predict the behavior of both attackers and protective systems. The following cognitive models in cyber systems are described: symbolic modeling (rule-based modeling) is used to build intrusion detection systems (IDS) that analyze network traffic for known attacks; network modeling (modeling using neural networks) includes anomaly detection systems that analyze typical network behavior; Bayesian models (probabilistic modeling) help predict risks and the probability of a successful attack on a specific system; agent-based modeling is used to simulate cyberattacks and test the resilience of systems. It was determined that the use of hybrid models that combine the above is effective. The challenges of implementing cognitive modeling in the security field are highlighted. These are the difficulties associated with the need for large volumes of qualitative data on the behavior of attackers, the complexity of modeling human behavior, and ethical issues. The results of the study can be used as educational material for students of the specialty F5 Cybersecurity and Information Protection.

Managers rarely have deep knowledge of cyber security and yet are expected to make decisions with cyber security implications for software-based systems. We investigate the decision-making conversations of seven teams of senior managers from the same organisation as they complete the Decisions & Disruptions cyber security exercise. We use grounded theory to situate our analysis of their decision-making and help us explore how these complex socio-cognitive interactions occur. We have developed a goal-model (using iStar 2.0) of the teams’ dialogue that illustrates what cyber security goals teams identify and how they operationalise their decisions to reach these goals. We complement this with our model of cyber security reasoning that describes how these teams make their decisions, showing how each team members’ experience, intuition, and understanding affects the team’s overall shared reasoning and decision-making. Our findings show how managers with little cyber security expertise are able to use logic and traditional risk management thinking to make cyber security decisions. Despite their lack of cyber security–specific training, they demonstrate reasoning that closely resembles the decision-making approaches espoused in cyber security–specific standards (e.g., NIST/ISO). Our work demonstrates how organisations and practitioners can enrich goal modelling to capture not only what security goals an organisation has (and how they can operationalise them) but also how and why these goals have been identified. Ultimately, non–cyber security experts can develop their cyber security model based on their current context (and update it when new requirements appear or new incidents happen), whilst capturing their reasoning at every stage.

In recent years, the application of Big Data analytics in public safety and emergency management has become increasingly important. With the growing volume of data generated by various sources such as social media, IoT devices, sensors, and emergency calls, Big Data technologies offer the potential to enhance decision-making processes, improve response times, and mitigate risks during emergencies. This paper explores the role of Big Data analytics in public safety, highlighting its use in disaster prediction, resource allocation, real-time decision-making, and crisis management. The paper also discusses the challenges faced by agencies in implementing Big Data solutions, including data privacy, security concerns, and interoperability issues, and provides recommendations for overcoming these obstacles to maximize the benefits of Big Data in emergency management.

Social media use, community participation and psychological well-being among individuals with serious mental illnesses

Enterprise economic efficiency is an important indicator for measuring the input and output of enterprises, and it has a certain guiding role in the formulation of enterprise management decisions. This paper constructs an enterprise economic efficiency assessment model using big data analysis and explores the application of big data analysis in enterprise management decision-making. First, we extract the principal component factors for measuring enterprise economic efficiency indexes using principal component analysis, and then construct the comprehensive evaluation function of enterprise economic efficiency based on this. The particle swarm algorithm is used to optimize the BP neural network, and the PSO-BP neural network model is constructed to intelligently evaluate the economic efficiency of the enterprise. This paper extracts three principal components of enterprise economic efficiency, namely “enterprise solvency and profitability,” “enterprise operating ability,” and “net asset growth ability,” and achieves a cumulative variance explanation rate of 84.431%. The PSO-BP neural network model and the standard BP neural network model satisfy the error criterion of MAPE<0.001 when they are trained about 170 times and 820 times, respectively, and the number of operations of the former is only about 20.73% of that of the latter, which greatly shortens the running time of the model and indicates that the PSO-BP-based enterprise economic efficiency assessment model in this paper is practical. Practicality. This paper serves as a reference for the application of big data analysis in enterprise economic benefit assessment and management decision formulation.

The rapid evolution of machine learning (ML) and big data analytics has modernized medical decision-making procedure, offering promising path for improving classification precision and ultimately, patient outcomes. This research inspects methodologies for enhancing the classification accuracy of medical decision systems by leveraging ML algorithms and big data analytics procedure. In this study, a broad evaluation of existing literature on ML applications in healthcare and medical decision-making is carried out to discover current challenges and potential areas for improvement. The research explores the integration of diverse data sources, including electronic health records (EHRs), medical imaging, genomic data, and patient-generated data, to build robust predictive models. Moreover, the research emphasizes the importance of interpretability and transparency in ML models for medical decision-making, particularly in critical healthcare settings where the rationale behind predictions is crucial. Techniques for model explainability, such as feature importance analysis and model-agnostic interpretability methods, are explored to enhance trust and adoption of ML-driven decision systems by healthcare professionals. Furthermore, the study investigates advanced ML algorithms such as deep learning, ensemble methods, and feature engineering techniques to extract meaningful patterns from large and complex medical datasets. Through experimentation with real-world medical datasets, the efficacy of these algorithms in improving classification accuracy is evaluated and compared against traditional methods. The result of this research contributes to the advancement of ML-driven medical decision systems by providing insights into strategies for improving classification accuracy, thereby facilitating more exact diagnosis, prognosis, and treatment recommendations. Ultimately, the integration of ML and big data analytics holds immense potential for revolutionizing healthcare delivery and improving patient outcomes.