New Tools for Cyber Security Using Blockchain Technology and Avatar-Based Management Technique

The chapter introduces the perspectives on the use of avatar-based management techniques for designing new tools to improve blockchain as technology for cyber security issues. The purpose of this chapter was to develop an avatar-based closed model with strong empirical grounding that provides a uniform platform to address issues in different areas of digital economy and creating new tools to improve blockchain technology using the intelligent visualization techniques. The authors show the essence, dignity, current state, and development prospects of avatar-based management using blockchain technology for improving implementation of economic solutions in the digital economy of Russia.

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.

Intelligent Log Analysis Using Machine and Deep Learning

Computers generate a large volume of logs recording various events of interest. These logs are a rich source of information and can be analyzed to extract various insights about the system. However, due to its overwhelmingly large volume, logs are often mismanaged and not utilized effectively. The goal of this chapter is to help researchers and industrial professionals make more informed decisions about their logging solutions. It first lays the foundation by describing log sources and format. Then it describes all the components involved in logging. The remainder of the chapter provides a survey of different log analysis techniques and their applications, consisting of conventional techniques using rules and event correlators that can detect known issues, plus more advanced techniques such as statistical, machine learning, and deep learning techniques that can also detect unknown issues. The chapter concludes describing the underlying concepts of the techniques, their application to log analysis, and their comparative effectiveness.

Ensemble Learning Mechanisms for Threat Detection

Technology evolution in the network security space has been through many dramatic changes recently. Enhancements in the field of telecommunication systems invite fruitful security solutions to address various threats that arise due to the exponential growth in the number of users. It's crucial for upgrading the entire infrastructure to safeguard the system from specific threats. So, there is a huge demand for the learning mechanism to realize the behavior of attacks. Recent upcoming technologies like machine learning and deep learning can support in the process of learning the behavior of all types of attacks irrespective of their deployment criteria. In this chapter, the analysis of various machine learning algorithms with respect to a few scenarios that can be adopted for the benefits of improving the security standard of the network. This chapter briefly discusses various know attacks and their classification and how machine learning algorithms can be involved to overcome the popular attacks. Also, various intrusion detection and prevention schemes were discussed in detail.

Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.

Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat

This chapter is devoted to studying the opportunities of machine learning with avatar-based management techniques aimed at optimizing threat for cyber security professionals. The authors of the chapter developed a triangular scheme of machine learning, which included at each vertex one participant: a trainee, training, and an expert. To realize the goal set by the authors, an intelligent agent is included in the triangular scheme. The authors developed the innovation tools using intelligent visualization techniques for big data analytic with avatar-based management in sliding mode introduced by V. Mkrttchian in his books and chapters published by IGI Global in 2017-18. The developed algorithm, in contrast to the well-known, uses a three-loop feedback system that regulates the current state of the program depending on the user's actions, virtual state, and the status of implementation of available hardware resources. The algorithm of automatic situational selection of interactive software component configuration in virtual machine learning environment in intelligent-analytic platforms was developed.

Healthcare Information Security in the Cyber World

Integrity, efficiency, and accessibility in healthcare aren't new issues, but it has been only in recent years that they have gained significant traction with the US government passing a number of laws to greatly enhance the exchange of medical information amidst all relevant stakeholders. While many plans have been created, guidelines formed, and national strategies forged, there are still significant gaps in how actual technology will be applied to achieve these goals. A holistic approach with adequate input and support from all vital partakers is key to appropriate problem modeling and accurate solution determination. To this effect, this research presents a cognitive science-based solution by addressing comprehensive compliance implementation as mandated by the Health Insurance Portability and Accountability Act, the certified Electronic Health Record standard, and the federal Meaningful Use program. Using the developed standardized frameworks, an all-inclusive technological solution is presented to provide accessibility, efficiency, and integrity of healthcare information security systems.

A Novel Bat Algorithm for Line-of-Sight Localization in Internet of Things and Wireless Sensor Network

WSNs have recently been extensively investigated due to their numerous applications where processes have to be spread over a large area. One of the important challenges in WSNs is secure node localization. Its main objective is to protect the circulated information in WSN for any attack with low energy. For this reason, recent approaches relying on swarm intelligence techniques are called and the node localization is seen as an optimization problem in a multi-dimensional space. In this chapter, the authors present an improvement to the original bat algorithm for information protecting during the localization task. Hence, the proposed approach computes iteratively the position of the nodes and studied the scalability of the algorithm on a large WSN with hundreds of sensors that shows pretty good performance. Moreover, the parameters are simulated in different scenarios of simulation. In addition, a comparative study is conducted to give more performance to the proposed algorithm.

Password-Less Authentication

Over the years, passwords have been our safeguards by acting to prevent one's data from unauthorized access. With the advancement of technologies, the way we have been using passwords has changed and transformed into much secure yet more user friendly than they were ever been in the past. However, the vulnerabilities identified and observed in this traditional system has motivated industry and researchers to find some alternate where there is no threat like stealing, hacking, and cracking of password. This chapter discusses the major developed password-less authentication techniques in detail and also puts an effort to explain the in-depth details along with the working principle of each of the technique through a use-case diagram. It would be of great benefit and contribution to the callow trying to explore research opportunities in this area.

A Machine Learning Approach for Predicting Bank Customer Behavior in the Banking Industry

Currently, Chinese commercial banks are facing extremely tremendous pressure, including financial disintermediation, interest rate marketization, and internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. Hence, it is very important to store, process, manage, and analyze the data to extract knowledge from the customer to predict their investment direction in future. Customer retention and fraud detection are the main information for the bank to predict customer behavior. It may involve the privacy data and sensitive data of the customer. Data security and data protection for the machine learning prediction is necessary before data collection. The research is focused on two parts: the first part is data security of machine learning and second part is machine learning prediction. The result is to prove the data security for the machine learning is important. Using different machining learning analysis tool to enhance the performance and reliability of machine learning applications, the customer behavior prediction accuracy can be enhanced.