Special Issue "Enterprise Security, Privacy and Risk for Internet of Things and Big Data (ESPR-IoTBD)"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Victor Chang
Website
Guest Editor
Computational Systems Biology and Data Analytics (CBD) Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley, TS1 3BX, UK
Interests: Internet of Things; Big Data; Industry 4.0; Security/Risk and Cloud/Edge/Fog Computing
Dr. Gary Wills
Website
Guest Editor
University of Southampton, Southampton SO17 1BJ, United Kingdom
Interests: security; big data; knowledge management; cloud computing; games
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Special Issue Information

Dear Colleagues,

Enterprise Security, Privacy, and Risk (ESPR) is the key to achieving global information security in business and organizations. The Internet of Things and Big Data (IoTBD) provide a new paradigm for enterprise, necessitating special attention to securing businesses. However, this new trend needs to be more systematically assessed with respect to ESPR, which is a factor in sustaining cloud technology by building-in trust. For example, current challenges with cyber security and application security flaws are highlighting important lessons that, once learned, will thereby lead to the adoption of best practices. Similarly, as the demand for IoTBD services increases, the importance of security, privacy, and risk is always growing. We survey the importance of ESPR as a unique and rising field to ensure all aspects of both security and risks can be identified, surveyed, tested, prototyped, and minimized with recommendations and lessons learned and disseminated. The scope of ESPR has expanded into risk management/analysis, the management of future technologies such as the Internet of Things and Big Data, and modern ethical hacking methods in many disciplines such as finance, healthcare, government, education, etc. We need to provide a robust and enhanced level of security. As a result, we seek high quality papers demonstrating proofs-of-concept, prototype, and successful implementation for IoTBD in ESPR.

Topics include the followings but not limited to:

  • Algorithms, software engineering, and development for ESPR
  • System design and implementation for ESPR
  • Testing (software engineering; penetration; product development) for ESPR
  • Encryption (all aspects) for ESPR
  • Firewall, access control, identity management for ESPR
  • Experiments of using security solutions and proof-of-concepts for ESPR
  • Large-scale simulations in the Cloud, Big Data, and Internet of Things for ESPR
  • Intrusion and detection techniques for ESPR
  • Social engineering and ethical hacking: techniques and case studies for ESPR
  • Risk Modeling, business process modeling, and analytics for ESPR
  • Financial modeling, financial process modeling, and financial risk analysis for ESPR
  • Supply Chain and Operations for ESPR
  • Trust and privacy for ESPR
  • Data security, data recovery, and disaster recovery for ESPR
  • Data center management for ESPR
  • Risk management and control for ESPR
  • Neutrosophic and formal methods for ESPR
  • Emerging issues and recommendations for organizational security for ESPR
  • Social network analysis, emerging issues in social networks for ESPR
  • Quantitative analysis and regression for ESPR
  • Architecture (technical or organizational) for ESPR
  • Case studies for ESPR
Prof. Dr. Victor Chang
Dr. Gary Wills
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Security and Privacy
  • Risk
  • Internet of Things
  • Big Data
  • Enterprise Security
  • Privacy and Risk (ESPR) for various disciplines

Published Papers (2 papers)

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Research

Open AccessArticle
Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
Appl. Sci. 2020, 10(9), 3145; https://doi.org/10.3390/app10093145 - 30 Apr 2020
Cited by 1
Abstract
Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk [...] Read more.
Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research. Full article
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Open AccessArticle
A Bipolar Neutrosophic Multi Criteria Decision Making Framework for Professional Selection
Appl. Sci. 2020, 10(4), 1202; https://doi.org/10.3390/app10041202 - 11 Feb 2020
Abstract
Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity [...] Read more.
Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results. Full article
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