Data Mining - Concepts and Techniques.

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

User Centered Design

The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes virtualized RANs where disaggregated components are connected via open interfaces and optimized by intelligent controllers. The result is a new paradigm for the RAN design, deployment, and operations: O-RAN networks can be built with multivendor, interoperable components, and can be programmatically optimized through a centralized abstraction layer and datadriven closed-loop control. Therefore, understanding O-RAN, its architecture, its interfaces, and workflows is key for researchers and practitioners in the wireless community. In this article, we present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early research results. We provide a deep dive of the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges of O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, security, and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development. 

/pdf/understanding-o-ran-architecture-interfaces-algorithms-1qe3xomy.pdf

Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges

Big Data, Fast Data and Data Lake Concepts☆

Keeping up with the burgeoning Internet of Things (IoT) requires staying up to date on the latest network attack trends in dynamic and complicated cyberspace, and take them into account while developing holistic information security (IS) approaches for the IoT. Due to multiple vulnerabilities in the IoT foundations, many targeted attacks are continuing to evolve. This survey of related work in the very specialized field of IS assurance for the IoT develops a taxonomy of typical attacks against IoT assets (with special attention to IoT device protection). Based on this taxonomy, the key directions for countering these attacks are defined. According to the modern demand for the IoT and big IS-related data processing, we propose applying the Security Intelligence approach. The results obtained, when compared with the related work and numerous analogues, are based on the following research methodology: view the IoT as a security object to be protected, leading to understanding its vulnerabilities and possible attacks against the IoT exploiting these vulnerabilities, and from there approaches to protecting the IoT. A few areas of the future research, among which the IoT operational resilience and usage of the blockchain technology seem to us the most interesting, are indicated.

Internet of Things: information security challenges and solutions

At present information security (IS) incidents have become not only more numerous and diverse but also more damaging and disruptive. Preventive controls based on the IS risk assessment results decrease the majority but not all the IS incidents. Therefore, an IS incident management system is necessary for rapidly detecting IS incidents, minimizing loss and destruction, mitigating the vulnerabilities that were exploited and restoring the Internet of Things infrastructure (IoTI), including its IT services. These systems can be implemented on the basis of a Security Operations Center (SOC). Based on the related works a survey of the existing SOCs, their mission and main functions is given. The SOCs' classification as well as the key indicators of IS incidents in IoTI are proposed. Some serious first-generation SOCs' limitations are defined. This analysis leads to the main area of further research launched by the author.

Security Operations Centers for Information Security Incident Management

The paper suggests a technique that allows to automate schemes that generates new criminal cases for money laundering and counter financing of terrorism (ML/CFT), which are based on ML/CFT typologies but do not appear as their exact copies. This feature hinders an automated system from making a decision about their exact coincidence or its absence while comparing case objects and links among them and links in ML/CFT typologies. Possibilities and advantages of application of Big Data for financial investigation data analysis and processing are also explored. The visualization of ML/CFT typologies with the use of graphs is considered. The article proposes a technique for generating variants of typologies (for example, "Peso" typology, "commission scheme") based on cases built on typologies. A program for implementation and verification of this technique was written and successfully tested on case graphs built on typologies.

Applying Big Data Technologies to Detect Cases of Money Laundering and Counter Financing of Terrorism

Today the information security (IS) of data mining is the crucial and comprehensive issue for organizations of the different spheres and size. The main challenges of Big Data IT are management of large amounts of heterogeneous information and providing its availability. Big Data protection against unauthorized access and corruption (keeping its confidentiality and integrity) as well as availability maintenance form the key research priorities in this field. The issues related to providing these Big Data features are considered in the paper. The existing approaches to their solution are analyzed. Some concepts for their improvement while designing the secure Big Data mining algorithm are formulated in accordance to IS properties.

Survey of Big Data Information Security

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