Transformative computing in security, big data analysis, and cloud computing applications
Lidia Ogiela, Fang−Yie Leu, Ugo Fiore · Concurrency and Computation Practice and Experience · 2021
In advanced data processing systems, one of the most important paradigms for distributed data analysis is innovative and transformative computing approaches. Such solutions allow not only data analytics tasks to be facilitated, but also intelligent and secure information analysis oriented especially for applications of new technologies and computational intelligence techniques. Nowadays there is a great demand to efficiently store and analyze a huge amount of information, originated from distributed sources or sensors, as well as an expectation to manage such information in a secure manner for applications in ubiquitous and mobile computing. The possibility of creation and development of such computation technologies will be connected with the introduction of new transformative computing procedures dedicated to security, big data analysis and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for transformative computing technologies, data security, security protocols, and distributed data analysis will form the subject of this Transformative 2020 special issue. The main topics of transformative computing in security, big data analysis and cloud computing applications presented at Transformative 2020 are primarily oriented at new computational approaches for big data and cloud security, transformative computing applications, personalized cryptography and biometric security, ambient intelligence, innovative security and privacy protocols, security of cognitive information systems, cryptography and secret data management, computational intelligence in data and services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, cognitive approaches for big data analytics, advanced cognitive steganography systems, behavioral features in data analysis and security solutions, and transformative approaches for big data analytics. This special issue features 12 papers, which present high quality scientific research, and interesting cutting-edge topics. The first paper entitled “Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy-analytic hierarchy process” by Park et al.1 proposes a new system that integrates a multi advisory function. The proposed solution starts from problem definition and continues to define the required solving task. Such technology supports customized information on defining problems and enables the definition of the requirements to characterize user features. The second paper “Time-based legality of information flow in the capability-based access control model for the Internet of Things” by Nakamura et al.2 introduces a new idea of time based legality of information flow. In the capability-based access control (CBAC) model, each subject has a capability token with which users can effectively manage a device. The authors propose a time-based operation interruption protocol (TBOI) to prevent illegal information—at any given time and at a later date. The third paper entitled “Transformative and Cognitive Approaches to Information Retrieval and Security Procedures” by Ogiela3 describes cognitive approaches in data security and a transformative computing paradigm dedicated to creation of human-centered security protocols. Such transformative computing applications focus on data exploration in distributed systems. The fourth paper “A Machine Learning-Based Memory Forensics Methodology for TOR Browser Artifacts” by Pizzolante et al.4 presents a bottom-up formal investigation model for the memory forensics of the Tor Browser. This methodology was developed based on a bottom-up logical approach for collecting information from different abstraction levels. The fifth paper entitled “Healthcare Fraud Detection Using Primitive Sub Peer Group Analysis” by Settipalli and Gangadharan5 presents new algorithms for identifying suspicious behaviors in health insurance. In this paper, a primitive sub peer group analysis (PSPGA) based on peer group analysis (PGA) and pattern interpretation and analysis is proposed for identifying user behaviors. The PSPGA recognizes drifts and classifies them as correct or fraudulent. The sixth paper “New Cognitive Sharing Algorithms for Cloud Service Management” by Ogiela and Ogiela6 presents new algorithms based on the meaning description dedicated to data analysis and security processes. The authors describe the possibility of applying such algorithms, depending on the structure of the target system, especially in cloud computing. This paper also discusses examples of two-stage secret protection algorithms—the simple data protection and the information set with semantics. The seventh paper entitled “Protocol Fuzzing to Find Security Vulnerabilities of RabbitMQ” by Kwon et al.7 shows a new fuzzy protocol for systems and service communications. A message broker named RabbitMQ is developed to find unknown vulnerabilities inherent in software. Their simulations demonstrate that the proposed algorithm is able to solve tasks by using the RabbitMQ especially in data security processes. The eighth paper “A deep learning-based indoor-positioning approach using received strength signal indication and carrying mode information” by Lin et al.8 describes a new indoor positioning scheme—learning-based indoor positioning system (LEIPS) which is used for identification of smartphone users by using inertial sensors and deep learning algorithms. Their experimental results demonstrate that the LEIPS has reached 96% of positioning accuracy. The ninth paper entitled “Optimizing Resource Scheduling Based on Extended Particle Swarm Optimization in Fog Computing Environments” by Narayana et al.9 introduces the extended particle swarm optimization (EPSO) algorithm which was developed with additional gradient method for optimizing scheduling tasks in cloud-fog environments. It also improves the efficiency of data analysis and minimizes the time of their implementation. The tenth paper “Population Data Mobility Retrieval at Territory of Czechia in Pandemic Covid-19 Period” by Platos et al.10 addresses the selection and collection steps of data analysis on mobile phones at the Czech Republic during the Covid pandemic. A data collection architecture is then proposed for spatial temporal mobility analysis. The analysis precision including the pandemic and non-pandemic periods is also shown. The eleventh paper entitled “Design and Analysis of Efficient Neural Intrusion Detection for Wireless Sensor Networks” by Batiha and Krömer11 analyses the acceleration of a neural intrusion detection model. The model was developed to detect intrusion/malicious behaviors for wireless sensor networks. The authors present their computational experiments with classification accuracy and training efficiency on different devices. In the last paper “Improved Publicly Verifiable Auditing Protocol for Cloud Storage” by Zhang et al.,12 the authors describe an outsourcing data protocol for cloud systems which is one of the new full integrity cloud auditing protocols. In addition to identifying its weaknesses, authors also analyze this protocol and present its security issues. This special issue introduces new solutions of transformative computing paradigms on different important topics. A part of them focuses on security and system protection. Some solve urgent network problems. All these techniques are oriented from information flow processes and machine learning in transformative computing, cognitive and semantic description of data management and security, fuzzy protocols and deep learning positioning, intrusion detection, fog-cloud applications, and big data collection and analysis processes. The wide range and impacts of the presented papers indicate an extraordinary variety of transformative computing paradigms, applications, and approaches. The authors also raise important open topics and their solutions. Following that, authors also show the possibility of further development on the discussed aspects. We would like to specially thank the Editors in Chief—Professor Geoffrey Fox, who first gave the opportunity of publishing this issue, and Professor David W. Walker who led this work at all times to the end. We are especially grateful for the opportunity to present this Special Issue and for their great kindness and help, as well as the unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience. We would also like to thank all the authors who have submitted their papers to this Special Issue. We congratulate all authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science, show new directions for research, as well as an innovative view of the previously developed experience and science.