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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
ROBOTIC PROCESS AUTOMATION Presenting the latest technologies and practices in this ever-changing field, this groundbreaking new volume covers the theoretical challenges and practical solutions for using robotics across a variety of industries, encompassing many disciplines, including mathematics, computer science, electrical engineering, information technology, mechatronics, electronics, bioengineering, and command and software engineering. Robotics is the study of creating devices that can take the place of people and mimic their behaviors. Mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, mathematics, and other subjects are all included in robotics. Robots can be employed in a variety of scenarios and for a variety of objectives, but many are now being used in hazardous areas (such as radioactive material inspection, bomb detection, and deactivation), manufacturing operations, or in conditions where humans are unable to live (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). Walking, lifting, speaking, cognition, and any other human activity are all attempted by robots. Many of today's robots are influenced by nature, making bio-inspired robotics a growing area. Defusing explosives, seeking survivors in unstable ruins, and investigating mines and shipwrecks are just a few of the activities that robots are designed to undertake. This groundbreaking new volume presents a Robotic Process Automation (RPA) software technique that makes it simple to create, deploy, and manage software robots that mimic human movements while dealing with digital systems and software. Software robots can interpret what's on a screen, type the correct keystrokes, traverse systems, locate and extract data, and do a wide variety of predetermined operations, much like people. Software robots can do it quicker and more reliably than humans, without having to stand up and stretch or take a coffee break.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 A Comprehensive Study on Cloud Computing and its Security Protocols and Performance Enhancement Using Artificial Intelligence
1.1 Introduction
1.2 Aim of the Study
1.3 Architecture of Cloud Computing
1.4 The Impact of Cloud Computing on Business
1.5 The Benefits of Cloud Computing on Business
1.6 Generic Security Protocol Features
1.7 Cloud Computing Security Protocol Design
1.8 AI Based Cloud Security
1.9 Various Neuronal Network Architectures and Their Types
1.10 Conclusion
Acknowledgement
References
2 The Role of Machine Learning and Artificial Intelligence in Detecting the Malicious Use of Cyber Space
2.1 Introduction
2.2 Aim of the Study
2.3 Motivation for the Study
2.4 Detecting the Dark Web
2.5 Preventing the Dark Web
2.6 Recommendations
2.7 Conclusion
Acknowledgement
References
3 Advanced Rival Combatant LIDAR-Guided Directed Energy Weapon Application System Using Hybrid Machine Learning
3.1 Introduction
3.2 Aim of the Study
3.3 Motivation for the Study
3.4 Nature of LASERS
3.5 Ongoing Laser Weapon Projects
3.6 Directed Energy Weapons (DEWs)
3.7 LIDAR Guided LASER Weapon System (LaWS) Requirements
3.8 Methodology
3.9 Conclusion
Acknowledgement
References
4 An Impact on Strategical Advancement and Its Analysis of Training the Autonomous Unmanned Aerial Vehicles in Warfare [Theme - RPA and Machine Learning]
4.1 Introduction
4.2 Aim of the Study
4.3 Motivation for the Study
4.4 Supervised and Unsupervised Machine Learning for UAVs
4.5 Unsupervised Solution
4.6 Conclusion
4.7 Scope for the Future Work
Acknowledgement
References
5 FLASH: Web-Form’s Logical Analysis & Session Handling Automatic Form Classification and Filling on Surface and Dark Web
5.1 Introduction
5.2 Literature Review
5.3 How FLASH Offers Better Results
5.4 Methodology
5.5 Results
5.6 Limitations and Future Work
5.7 Conclusion
References
6 Performance Analysis of Terahertz Microstrip Antenna Designs: A Review
6.1 Introduction
6.2 Microstrip Antenna Design
6.3 Challenges of Terahertz Antenna Development
6.4 Antenna Performance Attributes
6.5 Comparative Analysis of Microstrip THZ Antennas
6.6 Conclusion
References
7 Smart Antenna for Home Automation Systems
7.1 Introduction
7.2 Home Automation Antenna Geometry and Robotics Process Automation
7.3 Results for Home Automation Smart Antenna
7.4 Conclusion
References
8 Special Military Application Antenna for Robotics Process Automation
8.1 Introduction
8.2 Special Military Application Antenna for Robotics Process Automation
8.3 Results for Special Military Application Antenna
8.4 Conclusion
References
9 Blockchain Based Humans-Agents Interactions/Human-Robot Interactions: A Systematic Literature Review and Research Agenda
9.1 Introduction
9.2 Conceptual Foundation
9.3 Motivation
9.4 Blockchain and Robotics Overview
9.5 Human-Robot Interaction
9.6 Applications of HRI
9.7 Transactions between Robots and Human Beings
9.8 Escrow Services
9.9 Challenges for HRI
9.10 Discussion and Future Work
9.11 Conclusion
References
10 Secured Automation in Business Processes
10.1 Introduction
10.2 Literature Survey
10.3 Background
10.4 Proposed Model
10.5 Analysis of the Work
10.6 Conclusion
References
11 Future of Business Organizations Based on Robotic Process Automation: A Review
11.1 Introduction
11.2 Literature Review
11.3 Technology: A Need of Robotic Process Automation
11.4 Business Enterprise
11.5 Conclusion and Future Scope
References
12 Comparative Overview of FER Methods for Human-Robot Interaction Using Review Analysis
12.1 Introduction
12.2 FER Method Review Based Analysis
12.3 Feature Extraction Techniques
12.4 Conclusion
References
13 Impact of Artificial Intelligence on Medical Science Post Covid 19 Pandemic
13.1 Introduction
13.2 Types of AI Relevant to Healthcare
13.3 Diagnosis and Treatment Application
13.4 Limitation of Artificial Intelligence in Medical Science
13.5 The Future of AI in Healthcare
13.6 Conclusion
References
14 Revolutionizing Modern Automated Technology with WEB 3.0
14.1 Introduction
14.2 What is WEB 3.0: Definitions
14.3 Features & Characteristics
14.4 Implementation
14.5 Inventions around Modern Technology
14.6 Conclusion
Acknowledgement
References
15 The Role of Artificial Intelligence, Blockchain, and Internet of Things in Next Generation Machine Based Communication
15.1 Introduction
15.2 Blockchain
15.3 Internet of Things
15.4 Convergence of Blockchain, Internet of Things, and Artificial Intelligence
15.5 Block Chain for Vehicular IoT
15.6 Convergence of IoT with Cyber-Physical Systems
15.7 Conclusion
References
16 Robots, Cyborgs, and Modern Society: Future of Society 5.0
16.1 Introduction
16.2 Comparing Humans, Cyborgs, and Robots
16.3 Some Philosophical Aspects
16.4 Reproduction or Replication
16.5 Future of our Society
16.6 Implications
16.7 Conclusion
References
17 Security and Privacy of Blockchain-Based Robotics System
17.1 Introduction
17.2 Security and Privacy Concerns
17.3 Security and Privacy Requirements
17.4 Consensus Algorithms
17.5 Privacy and Security Techniques
17.6 Conclusion
References
18 Digital Footprints: Opportunities and Challenges for Online Robotic Technologies
18.1 Introduction
18.2 Proposed Methodology
18.3 Conclusion
Acknowledgement
References
19 SOCIAL MEDIA: The 21st Century’s Latest Addiction Detracted Using Robotic Technology
19.1 Introduction
19.2 Proposed Methodology
19.3 Importance and Value of Internet
19.4 Effects of Online Addiction on Society
19.5 Challenges to Reduce Social Media Addiction
19.6 Challenges Future Impact of Social Media Addiction on Youth
19.7 Conclusion
Acknowledgement
References
20 Future of Digital Work Force in Robotic Process Automation
20.1 Introduction
20.2 Robotic Process Automation
20.3 Robotic Process Automation Operations
20.4 RPA-Operating Model Design
20.5 Who is Who in RPA Business?
20.6 Conclusion
References
21 Evolutionary Survey on Robotic Process Automation and Artificial Intelligence: Industry 4.0
21.1 Introduction
21.2 Robotic Process Automation
21.3 Artificial Intelligence and Industry 4.0
21.4 RPA Tools with IA Support
21.5 RPA Tools with IA Support
21.6 Conclusions
References
22 Advanced Method of Polygraphic Substitution Cipher Using an Automation System for Non-Invertible Matrices Key
22.1 Introduction
22.2 Significance of Advanced Methods of a Polygraphic Substitution Cipher
22.3 Related Work
22.4 Proposed Methodology
22.5 Exploration
22.6 Conclusions
References
23 Intelligence System and Internet of Things (IoT) Based Smart Manufacturing Industries
23.1 Introduction
23.2 Development of Artificial Intelligence
23.3 AI Evolution from Intelligent Manufacturing to Smart Manufacturing
23.4 IM and SM Comparison
23.5 Further Smart Manufacturing Development for Industry 4.0
23.6 Conclusion
References
24 E-Healthcare Systems Based on Blockchain Technology with Privacy
24.1 Introduction
24.2 Blockchains in Healthcare
24.3 Regulations and EHR Privacy
24.4 Issues with Migration
24.5 Blockchains: Unified or Multiple
24.6 Formation of a Unanimity
24.7 Access Control & Users
24.8 Conclusion
References
25 An Intelligent Machine Learning System Based on Blockchain for Smart Health Care
25.1 Introduction
25.2 Review of Literature
25.3 Use of Blockchain in the Healthcare System
25.4 Machine Learning Algorithms in the Medical Industry
25.5 Blockchain and Artificial Intelligence Solutions
25.6 Conclusions
References
26 Industry 4.0 Uses Robotic Methodology in Mechanization Based on Artificial Intelligence
26.1 Introduction
26.2 Mechanization of Robotic Processes
26.3 Industry 4.0 and Artificial Intelligence
26.4 RPM Outfits that Sustenance Artificial Intelligence
26.5 Discussion
26.6 Conclusions
References
27 RPA Using UiPATH in the Context of Next Generation Automation
27.1 Introduction
27.2 Traditional Approach vs RPA Approach
27.3 Related Work
27.4 Applications of RPA
27.5 Intelligent Process Automation
27.6 RPA and Blockchain
27.7 Implementation
27.8 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 3
Table 3.1
Table 3.2 LIDAR guided LASER weapon system requirements.
Chapter 5
Table 5.1 Summary of reductionist analysis [31].
Table 5.2 Feature distribution over literature of papers.
Table 5.3 Criteria for form classification.
Table 5.4 Manual surface web dataset.
Table 5.5 Manual dark web dataset.
Table 5.6 Accuracy of form classification on manual surface web.
Table 5.7 Correlation matrix for manual surface web dataset.
Table 5.8 Accuracy of form classification on manual dark web dataset.
Table 5.9 Correlation matrix for manual dark web dataset.
Table 5.10 results of form filling for surface web dataset.
Table 5.11 Results of form filling for dark web dataset.
Table 5.12 Automated surface web dataset.
Table 5.13 Automated dark web dataset.
Table 5.14 Classification of forms on automated surface web dataset.
Table 5.15 Classification of forms on automated dark web dataset.
Table 5.16 Input tag field distribution for automated surface web dataset.
Table 5.17 Input tag field distribution for automated dark web dataset.
Chapter 6
Table 6.1 Performance comparison of microstrip terahertz antennas.
Chapter 7
Table 7.1 Proposed smart antenna for home automation design description.
Table 7.2 Comparison of published smart antennas for home automation systems.
Table 7.3 Robotics process automation ‘smart antenna for home automation syste...
Chapter 8
Table 8.1 Geometry parameters of special military application antenna.
Table 8.2 Comparison of published planar antennas.
Table 8.3 Robotics process automation smart antenna for military application a...
Chapter 10
Table 10.1 Algorithm to generate hash code for identification of individual/ c...
Table 10.2 Generating identity bits for next cycle.
Table 10.3 Generating the hash code for the activity.
Chapter 12
Table 12.1 Comparative analysis from tabular structure.
Chapter 17
Table 17.1 Comparison of the consensus algorithms.
Chapter 21
Table 21.1 Comparison of goals associated to AI and various technologies.
Chapter 22
Table 22.1 Different studies of encryption/decryption techniques using automat...
Chapter 23
Table 23.1 AI development.
Table 23.2 Comparison of IM and SM.
Chapter 26
Table 26.1 Comparison of AI Goals.
Chapter 27
Table 27.1 Comparison of traditional approach vs RPA approach.
List of Illustrations
Chapter 1
Figure 1.1 Cloud computing.
Figure 1.2 Cloud computing and security protocols.
Figure 1.3 Cloud computing structure.
Chapter 3
Figure 3.1 Communication links between different objects with reference to the...
Figure 3.2 Faster R-CNN model for army position locating.
Figure 3.3 YOLO model for helmet and safety vest classification.
Figure 3.4 DenseNet model for fastness of chin strap on helmet recognition.
Chapter 5
Figure 5.1 Flowchart for FLASH.
Figure 5.2 Identifying forms and extracting fields.
Figure 5.3 Interpretation of form fields.
Figure 5.4 Generating and sending report.
Figure 5.5 Surface web results.
Figure 5.6 Dark web results.
Chapter 6
Figure 6.1 Terahertz radiation spectrum [3].
Figure 6.2 Structure of microstrip antenna.
Figure 6.3 Shapes of microstrip patch elements.
Figure 6.4 Microstrip antenna feeds.
Figure 6.5 Terahertz antenna challenges.
Chapter 7
Figure 7.1 Block diagram of home automation system using smart antenna.
Figure 7.2 Front view of 5G antenna.
Figure 7.3 Back view of 5G antenna.
Figure 7.4 Side view of 5G antenna.
Figure 7.5 Return loss (S
11
) curve.
Figure 7.6 VSWR curve.
Figure 7.7 Gain of antenna.
Figure 7.8 Surface current distribution of antenna.
Chapter 8
Figure 8.1 Configuration of special military application antenna.
Figure 8.2 Prospective view of special military application antenna.
Figure 8.3 8 Stages of evolution of special military application antenna.
Figure 8.4 Gain of special military application antenna at 3 different frequen...
Figure 8.5 Radiation pattern of special military application antenna at 2 diff...
Figure 8.6 Simulated VSWR and S11 curve.
Figure 8.7 Surface current distribution.
Chapter 9
Figure 9.1 Human versus robot chess game and betting sequence diagram.
Figure 9.2 Example of autonomous vehicle smart contract ownership.
Figure 9.3 Smart contract partial interface definition.
Figure 9.4 Unilateral contract flow.
Chapter 10
Figure 10.1 Architecture of merkle tree in blockchain (Chen, Chou, & Chou, 201...
Figure 10.2 Comparison of security in the system.
Chapter 12
Figure 12.1 Facial expression recognition process.
Figure 12.2 Various FER methods’ accuracy levels.
Chapter 15
Figure 15.1 Sample Number – 1
st
Figure 15.2 Sample Number – 2
nd
.
Figure 15.3 Sample Number – 3
rd
.
Figure 15.4 Sample Number – 4
th
.
Chapter 17
Figure 17.1 Architecture of blockchain.
Figure 17.2 Blockchain-based cryptocurrency transaction.
Figure 17.3 Coin mixer.
Chapter 18
Figure 18.1 Carbon footprint comparison.
Chapter 19
Figure 19.1 Social network overview.
Figure 19.2 User activity design.
Chapter 20
Figure 20.1 RPA modelling.
Figure 20.2 System compliance.
Figure 20.3 RPA charting.
Chapter 22
Figure 22.1 Automation system perception.
Chapter 23
Figure 23.1 AI evolution from perspectives of content and control.
Figure 23.2 Intelligent manufacturing evolution along with AI.
Figure 23.3 Smart manufacturing exemplified as cyber-physical production syste...
Figure 23.4 Intelligent manufacturing versus smart manufacturing.
Figure 23.5 Wisdom manufacturing vs. other emerging manufacturing models with ...
Figure 23.6 Framework for SCPS-based manufacturing.
Chapter 24
Figure 24.1 Blockchain framework for business.
Figure 24.2 Blockchain based framework of e-healthcare model.
Figure 24.3 Solution for cooperative e-healthcare interoperability.
Figure 24.4 Increase in Ledger’s RAM use.
Chapter 25
Figure 25.1 Blockchain healthcare management system overview.
Figure 25.2 Architecture of blockchain and artificial intelligence in smart he...
Chapter 26
Figure 26.1 Structural design of industry 4.0.
Chapter 27
Figure 27.1 Process automation structure.
Figure 27.2 Destination folder.
Figure 27.3 Email automation.
Figure 27.4 Mail sequence.
Figure 27.5 Results after the bot is executed.
Figure 27.6 Source folder named “Resources”.
Figure 27.7 Moving file path.
Figure 27.8 Spreadsheet
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Robotic Process Automation
Edited by
Romil RawatRajesh Kumar ChakrawartiSanjaya Kumar SarangiRahul ChoudharyAnand Singh Gadwal
and
Vivek Bhardwaj
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-16618-3
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
The book discusses advanced working with digital systems and software. The book’s RPA (Robotic Process Automation) software approach makes it straightforward to build, use, and manage software robots that replicate human motion. Software robots are like humans in that they can read what’s on a screen, input the right keys, navigate systems, find, and extract data, and do a wide range of specified tasks. Without the need to get up and stretch or take a coffee break, software robots can complete the task faster and more accurately than people. Robotics encompasses a variety of disciplines, including mathematics, computer science, electrical engineering, information technology, mechatronics, electronics, bioengineering, and command and software engineering.
The science of building robots that can stand in for people and replicate their behavior is known as robotics. Robots are applicable to a wide range of situations and for a wide range of purposes, although many are currently used in dangerous environments (such as particle inspection, bomb detection, and disposal), manufacturing processes, or in environments where humans cannot survive (e.g., in space, underwater, in high heat, and for the clean-up and containment of hazardous materials and radiation). Robots try every human behavior, including moving, lifting, talking, thinking, and cognition. Bio-inspired automation is a developing field since so many of today’s robots are affected by nature. Some of the tasks include disarming explosives, searching for people amid unstable ruins, and looking into mines and wrecks.
The book’s primary objective is:
To present current trends and implications for robotics, big data, cloud computing, virtual reality, and digital communication technologies using robotic process automation systems.
To go through the current state of advanced modelling and the RPA project structure.
To analyse and offer innovative models, strategies, and initiatives for hardware and digital platforms.
1A Comprehensive Study on Cloud Computing and its Security Protocols and Performance Enhancement Using Artificial Intelligence
Srinivasa Rao Gundu1, Charanarur Panem2* and J. Vijaylaxmi3
1Department of Computer Science, Government Degree College-Sitaphalmandi, Hyderabad, Telangana, India
2School of Cyber Security and Digital Forensic, National Forensic Sciences University, Goa Campus, Goa, India
3PVKK Degree & PG College, Anantapur, Andhra Pradesh, India
Abstract
Since cloud computing is becoming an increasingly vital component of both big and small businesses, ensuring its integrity and confidentiality has emerged as a top priority in this space. There are a few different approaches that may be used to secure the cloud. Techniques are implemented through protocols. The protocols that are used in cloud computing may also be used in other types of security systems, such as authentication systems, mailing systems, and cryptonet systems. Cloud computing has challenges in the areas of security on-demand application resource management, and self-monitoring without delay. These challenges arise because of the massive amount of data that is made accessible via cloud computing. When it comes to improving the capabilities of security and privacy in cloud storage, Artificial Intelligence (AI) and machine learning have the potential to play a pivotal role. Therefore, incorporating methods for machine learning into the cloud that already exists with the potential to give enhanced efficiency.
Keywords: Simple Storage Service (S3), Elastic Compute Cloud (EC2), artificial neural network, cryptography, information and communication technology
1.1 Introduction
When it comes to cloud computing, several factors are working together to speed up its arrival and make it a reality sooner rather than later. Because of the development of more reasonably priced and powerful processors and the software as a service (SaaS) computing architecture, conventional data centres are being transformed into massive computing service pools (MCS). Because of the network’s increased capacity and robust and flexible connections, users may now subscribe to high-quality services that employ data and software solely hosted in faraway data centres.
When data is moved to the cloud, users save time and money by not having to worry about maintaining local hardware. Moving data to the cloud is beneficial to users. There are two well-known organizations that provide cloud computing services: Amazon S3 and Amazon Elastic Compute Cloud (EC2) [1].
Additionally, this shift in computing platforms frees local computers from the burden of storing their own data, making it feasible for modern internet-based online services to supply enormous amounts of storage space and computer capabilities that may be customized.
Consequently, the cloud service providers of the users control both the availability and the integrity of the users’ data as a direct consequence of this. There are several reasons why cloud computing presents substantial security issues. Data security is one of these issues, which has long been recognized as an important part of service quality. To begin, because users no longer have authority over their data, traditional cryptographic primitives cannot be directly employed in a cloud computing context [2].
It is because of this that cloud storage verification must be done without direct access to all of the data that is stored in the cloud. It becomes increasingly difficult to prove the authenticity of cloud-stored data when you consider the variety of data types that each user has access to, as well as their need for long-term confidence that their data is safe. Second, cloud computing is more than just a third-party data storage facility.
1.2 Aim of the Study
The objective of this project is to examine whether or not Artificial Intelligence (AI) and machine learning can play a major role in enhancing cloud storage facilities in terms of both security and privacy.
1.3 Architecture of Cloud Computing
A cloud computing architecture is a collection of cloud components that are only loosely linked to one another. The two components that may be separated to construct the cloud architecture are the front-end and the back-end.
A network, typically the Internet, serves as the linking mechanism between the system’s two ends. Figure 1.1 depicts a graphical representation of cloud computing architecture.
The “front end” of a cloud computing system is the element that interacts with users, or clients. It is made up of the interfaces and programmes needed to access cloud computing platforms, such as a web browser, and its primary component is known as the software stack.
In the background, cloud computing is often referred to as a system’s “back end.” It has all of the resources necessary to deliver cloud computing services. It includes, among other things, massive amounts of data storage, virtual machines, security measures, services, deployment methods, and servers [3].
Figure 1.1 Cloud computing.
1.4 The Impact of Cloud Computing on Business
In the world of information technology and cloud computing has become indispensable. Despite its popularity, many firms are reluctant to implement and utilize cloud computing for commercial and operational reasons due to uncertainty about its cost and security consequences. The fundamental appeal of cloud computing for organizations is its cost-effective-ness, whereas the most serious concern is security risks. Cloud computing security is a major concern in the workplace. In addition, various critical implications that enterprises should consider while using cloud computing are explored, as well as security methods for avoiding the highlighted cost and security difficulties [4].
Cloud computing has become a widely accepted and ubiquitous paradigm for service-oriented computing in which computer infrastructure and solutions are provided as a service. Through its characteristics (e.g., self-service on-demand, wide network access, resource pooling, and so on), the cloud has revolutionized the abstraction and use of computer infrastructure, making cloud computing popular. Security, on the other hand, is the most pressing issue and worries about cloud computing are growing as we see more and more innovative cloud computing platforms [5].
Cloud services, software, and infrastructure are clearly becoming more popular in the post-COVID-19 environment, since they can be accessed at any time and from any location. Several research projects and advancements have been suggested to address security threats. Nonetheless, new approaches to make the cloud safer have yet to be discovered. The majority of current cloud security approaches do not address the new sorts of security concerns that cloud computing infrastructure may encounter. As a result, they are unable to identify attacks or vulnerabilities that may originate from either the cloud service provider or the customer [6].
Cloud computing is based on the notion of providing all feasible services, such as software, IT infrastructure, and services to clients over the internet. Cloud computing systems are heterogeneous, large-scale groupings of autonomous computers with a flexible computational architecture. This technology is on the rise since it is becoming the preferred option for firms who do not want to deal with system maintenance or a development team in-house. Many companies, including Amazon Web Services (AWS), Google, IBM, Sun Microsystems, Microsoft, and others, are creating effective cloud products and technologies. Customers and the enterprise exchange data through virtual data centres with cloud technologies [7].
Furthermore, just a few previous studies have looked at the many tiers of cloud architecture. This study did an exhaustive assessment on the challenges that the cloud computing infrastructure encounters at various levels due to the relevance of examining such concerns (application, host, network, and data level). It also discusses the current solutions that have been employed to address these concerns. In addition, this report identifies certain outstanding difficulties that need to be addressed as well as future research prospects [8].
The term “cloud computing” may apply to both a platform, as well as a specific kind of application. The data of both users and corporations is kept safe on servers located in the cloud. The term may also apply to applications that may be downloaded from many websites on the internet. Cloud applications are stored on robust servers that are located in large data centres. These servers also host websites and online services. Several businesses in the information technology sector, including Google, Amazon, Microsoft, and others, are developing and marketing products and services that are hosted on the cloud [9].
1.5 The Benefits of Cloud Computing on Business
Cloud computing has various benefits for enterprises, but a few stick out:
Some of the cost advantages include decreased investment on technology infrastructure, lower capital costs, related savings, and convenience savings.
Some of the technological benefits include reducing maintenance, innovating in technology, devising diversity, and so on. Implementation is straightforward, modification is straightforward, and storage is expanded. Advantages for businesses, customization, adaptability, agility, creativity, selection, and service quality are all important considerations in maintaining vigilance and keeping ahead of the curve [11].
1.6 Generic Security Protocol Features
Generic security protocols as security system enablers offer network security services to various components of a cloud computing environment. These services are provided using generic security protocols. Initial local user authentication protocol, remote user authentication protocol, Single-Sign-On protocol, consuming protocol, cloud trust protocol, secure sessions protocol, and file transfer protocol are used.
Figure 1.2 Cloud computing and security protocols.
The protocols are based on the concepts of generic security objects and a modular security strategy. Each protocol is functionally complete, easy to integrate with other components, and transparent in terms of security credentials and characteristics. They also provide the same set of secure network services to all components of the cryptonet system. The Figure 1.2 shows about the cloud computing and security protocols architecture details.
1.7 Cloud Computing Security Protocol Design
Security protocols are designed in a modular fashion, with each module using a distinct implementation of the idea of generic security objects. This section provides an overview of general cloud computing protocols, as well as various security measures such as the cryptonet system.
1.7.1 Protocol for File Transfer (FTP)
The cloud may be used to store and access a broad range of file types inside an organization. Because a file may include any kind of digital information, such as a written document, image, artwork, video, sound, or piece of software, files can be transferred via the File Transfer Protocol (FTP) service. The File Transfer Protocol (FTP) is a standard protocol for sending data from one computer to another via the Internet. FTP is used to allow users to move files to the cloud. The user information comprises of the username and password, as well as the home directory. The File Transfer Protocol (FTP) is a file distribution protocol that may retrieve data both locally and remotely. FTP was formerly utilised to provide a locking scheme for both cloud and local data, but that practice has since been abandoned.
1.7.2 Local User Authentication Protocol (LUAP)
Users may authenticate themselves on a local level using the LUAP protocol. The local user authentication technique is used in cloud computing and other security systems. The local user authentication method is included within the system’s login module. It works with both the username/password authentication mechanism and the certificate-based authentication approach.
The workstation does an automatic examination of the installation environment and settings as soon as the operating system is initiated and then determines the suitable protocol. In order to activate, our system needs a user’s Personal Identification Number (PIN) and if set for username/ password authentication, it may additionally require the user’s fingerprint in addition to the PIN. It communicates with the Security Applet to get the username and password, then provides those credentials to the operating system’s native login mechanism. In order to accomplish authentication, the login module will query the user accounts database.
1.7.3 Protocol for Consumption
This protocol was developed with the supplier and the client in mind from the beginning. This protocol addresses concerns about privacy, reliability, and security. Computing in the cloud provides consumers and businesses with delivery platforms that are cost-effective, scalable, adaptable, and have a track record of success. The software-as-a-service model, more often known as SaaS, has the potential to save costs associated with the creation and maintenance of both hardware and software. Platform-as-a-service, also known as PaaS, has the potential to lower costs and simplify the process of acquiring, storing, and administering the software and hardware components of a platform. As a consequence of this, the consumption protocol is a key component to consider in designing the architecture of cloud computing. Cloud computing is presently being used by a very diverse assortment of companies, ranging in size from very small to very big.
1.7.4 Remote User Authentication Protocol
The RUAP protocol allows users to authenticate themselves from a remote location. Cloud computing is a newer kind of computer system that allows users to keep their work on the cloud and then upload and retrieve it as required. Cloud computing is rapidly gaining popularity, but there are still substantial challenges to overcome, such as security and privacy concerns.
Cloud computing is a resource that is available to everybody, which means that a user must connect to the cloud server using open networks in order to utilize it. In the event that a secure system is not built, a number of potential vulnerabilities might be exposed. It is possible for an attacker to have access to the personal information of a user. Because of this, one of the most significant challenges in cloud computing is maintaining the anonymity of users. The remote user authentication protocol is used as a security technique in cloud computing environments in order to ensure that data transmissions are kept secure.
1.7.5 Secure Cloud Transmission Protocol
SCT is a protocol that provides secure data transfer over the internet. The usage of cloud computing has grown in popularity as a result of the many advantages it provides. Small and large businesses alike are increasingly turning to cloud computing services to save costs and streamline operations. Encrypted data transport is required. There are several security concerns to be worried about in the context of cloud computing.
It is vital to interact in a secure way in order to resolve these challenges. Both the user datagram protocol (UDP) and the transmission control protocol (TCP) employ the secure transmission cloud protocol (UDP). It is commonly regarded as a cutting-edge protocol due to its ability to handle a broad range of infrastructure requirements for optimal data transfer over high-speed networks. UDP, or the User Datagram Protocol, is being used to build secure cloud communication protocols (UDT).
1.7.6 Protocol for Cloud Trust
The Cloud Trust Protocol (CTP) is a way that cloud clients may use to request digital data from cloud service providers and subsequently receive that data. This strategy is used to build a user’s faith in the owner of the cloud. Using this protocol, you may be able to create a secure method in a cloud computing environment. The usage of this protocol may improve the field of digital trust. Digital trust is the system that assures the security of digitally recorded information. Transparency in information is the foundation for digital trust, and as such, it is the key driver of value collection and return. CTP is used to establish digital trust between a cloud computing client and the provider, as well as to give transparency about the provider’s configuration vulnerabilities, authorization, accountability, and operational status conditions. This is performed by using cloud computing.
1.7.7 Protocol for Secure Single-Sign-On
Cloud computing and cloud protocol are two of today’s most well-known and fastest evolving technologies. They offer a highly reliable service architecture that can deliver cloud-based integrated services such as on-demand resource computation, resource or data storage or cumulative storage, and exceptionally fast network connectivity. It is probable that it will create a system that is quick and unquestionably successful, needing little in terms of resource management activities and providing an interface for service providers, allowing one to get effective cloud-based services via the usage of internet services.
Each time the client accesses one of today’s modern applications, they must memorize and use a new set of credentials. It may be difficult for a company to keep track of the various authentication methods and databases, particularly when each one is utilised by a different kind of organization. As a consequence, we want a dependable protocol that permits speedy sign-on, which will eventually result in a protocol for single sign-on. Clients may utilize a single-on protocol to perform a single sign-on to an identity provider that is trusted by the application that they want to access. Customers no longer need to confirm their identities to a variety of different applications several times, nor do they need to utilize various authentication techniques for each of their apps thanks to the single sign-on protocol.
1.7.8 Secure Session Protocol (SSP)
After the single-sign-on protocol has been successfully established, the secure session protocol is used to offer a session in cloud computing and other security systems. Other security systems may also utilize this protocol. The implementation of the single-sign-on protocol was fruitful, therefore this is the natural next step. The usage of a key exchange certificate may allow for the construction of the protocols necessary for a secure connection. After the key exchange certificate has been installed on both the client and the cloud server, it will be possible for the client and the cloud server to securely swap session-keys and session-ids with one another. A session key is used by the application server’s session protocol in order to facilitate the process of controlling the characteristics of encrypted sessions.
A session key is a randomly generated encryption and decryption key that is used to guarantee the security of a communications session between a user and another computer or between a client and a server. This might happen between a client and a server. Session keys are also known as symmetric keys. This is because the same key is used for both encryption and decryption.
This approach, known as session key derivation, is used to generate a session key from a hash value. To do this, the cryptderive key function is used. The key is encrypted using the recipient’s public key throughout the session and then included in each message transmitted. Because the amount of security given by session keys is related to how often they are used, those keys are renewed on a regular basis. A distinct session key may be used for each individual interaction.
1.7.9 Protocol for Authorization
In the context of cloud computing, the authorization protocol allows the customer, as well as the service provider to have more control over the level of permit security. The standard known as XACML is used as the foundation for our security system’s authorization rules. We used a system called Function-Based Access Control, which denotes that an authorized person, such as a Security Administrator, is the one who organizes a group and decides the access level, role, and permissible activities for each individual member of the group.
A Policy Token with a Target object, which is used to determine the role that each individual member of the group plays, is produced by the Security Administrator. The target consists of the name of a group member, the name of a resource, and the activities that a group member is permitted to do in accordance with the resource authorization policy that has been set.
1.7.10 Protocol for Key Management
Cooperative cryptonet and cloud applications employ key exchange protocols to share group keys. Generic Key Distribution (GKD) complies to the GSAKMP standard. GKD manufactures, distributes, and rekeys keys.
Key exchange protocols are used to exchange group keys between cryptonet and cloud computing applications that operate in a cooperative environment. Generic Key Distribution (GKD) was developed to fulfill this need and adheres to the GSAKMP standard. GKD is accountable for all facets of key production, distribution, and rekeying.
GKD supports both Push and Pull operations, enabling the deployment of shared keys. In addition, it distributes keys in combination with the Secure Application Server. This module uses the PEP component of an application server to create shared-key authorization constraints since it acts as a component. When a group member requests a group-key, he or she establishes a secure connection to the Secure Application Server using Single Sign-On.
The group member used a smart card to access the Secure Application Server’s PEP in order to acquire a SAML ticket. After a successful authorization, GKD will transmit the group-key across a secure communication channel to the authorized member of the group. The two kinds of cryptographic keys are secret keys and pairs of public/private keys. The safest method is using secret keys.
A number of other keys are used in cloud computing, including public/ private authentication and signature keys, public/private key establishment pairs, symmetric encryption and decryption keys, and symmetric message authentication codes. Public/private authentication, private/private signature, public/private key establishment, and symmetric key wrapping are all instances in which these pairs of keys are used in this way.
The group member uses a smart card to access the Secure Application Server’s PEP to acquire a SAML ticket. After successful authorization, GKD sends the group-key via a secure channel to the member. Cryptographic keys may be secret or public/private. Secret keys are the most secure.
Cloud computing uses public/private authentication and signature keys, public/private key establishment pairs, symmetric encryption/decryption keys, and symmetric message authentication codes. This includes public/ private authentication, private/private signature, public/private key establishment, and symmetric key wrapping. The Figure 1.3 shows about the cloud computing structure with sharing platforms.
Figure 1.3 Cloud computing structure.
1.8 AI Based Cloud Security
Artificial intelligence (AI)[10] has shown to be an essential component in the disciplines of cyber security and cloud computing security as we go ahead into the era of automation[12]. In light of the fact that AI is capable of rapid learning, it is of the utmost importance to concentrate on finding ways that AI may both improve security and specify how standards can be set around the appropriate use of it. This will ensure that businesses are prepared for the further development of AI.
The term “artificial intelligence” (AI) refers to software developed for computers that has the ability to solve problems and reason in the same way that a human would. The great majority of productive research and development that has been accomplished up to this point may be attributed to the field of machine learning (ML), which is a subfield of artificial intelligence that focuses on training computers to learn by applying algorithms to data.
The phrases “machine learning” (ML) and “artificial intelligence” (AI) are sometimes used interchangeably. AI stands for “artificial intelligence.” An issue must be able to be solved using data and there must be a sufficient amount of relevant data that can be acquired for it to be evaluated for a solution utilising artificial intelligence or machine learning.
In addition, there must be access to a large enough amount of computer power in order to complete the essential processing in a time frame that is acceptable.
The volume of data [13, 14] generated by cyber security [15, 16] systems is so large that no human team could possibly hope to process and evaluate it all. In order to identify potentially hazardous scenarios, machine learning algorithms examine all of this information. The more data it examines, the more patterns it finds and learns, which it can then use to identify deviations from the typical pattern flow. The more data it analyses, the more patterns it [17] finds and learns. These alterations have the potential to be seen as cyber threats [18].
For instance, machine learning keeps a record of activities that are regarded as typical, such as the time and date at which workers check in to their respective systems, the information that they often view, and many other traffic patterns and user behaviors. There are several circumstances in which these limits do not apply, such as being able to log on in the middle of the night.
As a direct result of this, possible dangers may be identified and dealt within a far shorter amount of time. Artificial intelligence may be used, using a strategy that is more data-driven, to uncover flaws and vulnerabilities that are now being exploited or that may be exploited in the future and to provide proactive warnings on such weaknesses and vulnerabilities. In order for this to operate, data that is entering and leaving protected end-points is analyzed and known behaviors and predictive analytics are used to locate and identify potential threats.
1.8.1 Event Detection and Prevention
When AI and machine learning technologies analyze data produced by systems and discover abnormalities, they may take a number of different actions in response, like alerting a person, blocking a specific user, or doing something else entirely. These tactics often result in events being noticed and halted within a matter of hours, therefore putting a stop to the transmission of potentially dangerous code and averting a data breach. Businesses may be able to gain days of notice and time to respond in advance of security issues by using this method, which involves reviewing and integrating data across geographies in real-time.
1.8.2 Delegate to Automated Technology
There are several security systems that can alert you to possible dangers or abnormalities. but automated solutions may eliminate a significant portion of this background noise, allowing you to concentrate on what really matters. When technologies like AI and machine learning are used to perform regular duties and first-level security assessments, security personnel are freed up to concentrate on more major or complicated risks.
This does not imply that these technologies can take the position of human analysts, however, since cyber assaults are often the result of a mix of human and machine activity, these attacks need reactions from both humans and machines. On the other hand, it enables analysts to prioritize their workload and complete their jobs in a more timely manner. To facilitate their business activities, corporations often make use of hundreds or even thousands of interconnected programs. Traditional computer systems store data in a variety of locations, which makes it difficult to ensure that all of those locations are in sync with one another.
With multitenancy Software as a Service (SaaS), which saves human resource, financial, and planning data in one application, all of this is much easier. This central design has many advantages, including the fact that all systems function under the same framework, which eliminates data inconsistencies. It also bridges the divide between the system and those who use it. Access control, on the other hand, must be prioritized.
Because today’s workforce is equipped with a range of devices, data is dispersed across several access points, increasing the risk of vulnerability. By prioritizing an access solution that incorporates inspection applications, establishing permissions, and defining rules, the appropriate individuals may have access to the tools they need to operate efficiently.
1.8.3 Machine Learning Algorithms in Cloud Computing
The ever-increasing volume of data that is being collected and processed at ever-increasing rates is a direct result of the ever-increasing number of internet-connected gadgets. This is especially critical in the case of an emergency requiring an instant reaction. When it comes to processing large amounts of data, the length of time it takes has become longer and longer.
The current cloud architecture is not the ideal solution for dealing with these circumstances since the data is routed to a variety of faraway cloud centres. An increase in productivity may be realized by adding machine learning algorithms to an already existent cloud. A huge amount of data is also available on the cloud that may be fed into machine learning algorithms.
In machine learning, clustering is a fundamental technique for organizing and categorizing data. “Clustering” is a term used to describe this basic process, which may subsequently be refined with additional cognitive and predictive algorithms. Machine learning and artificial intelligence techniques have just recently been used by data scientists for cloud computing.
Examples of Keras-based services include Amazon Web Services, IBM Watson, and Microsoft Cognitive AI. Machine learning and artificial intelligence also play a crucial role in addressing the requirements for computing that is both effective and efficient in this day and age of Internet of Things (IoT), Big Data Analytics, and Blockchain.
1.9 Various Neuronal Network Architectures and Their Types
There are three distinct categories of network architectures, which are as follows:
Single-Layer Feed-Forward Networks: The input layer of a network with a single layer of feed-forwarding is composed of source nodes and the neurons that are produced are the output. This is a network that uses feed forwarding.
Multilayer Feed Forward Networks: This network simply adds an additional layer that is concealed from view. A higher degree of statistic may be accomplished thanks to the hidden layer that is being used.
Recurrent Network: This network has at least one feedback loop in its structure. This loop, which boosts a neuron’s ability for learning by feeding its output back into its own input, is shown in
Figure 1.1
. Additionally, it results in enhanced performance.
1.10 Conclusion
Security methods used for authentication, secure communication, and permissions were examined as part of this study. Along with established technical and legal security requirements, the protocols are built on generic security objects as well. In the context of cloud computing, they maintain security credentials and protocol-specific features in a completely transparent way. In addition, the same attributes might be extended to other well-established methods. Examples of generic security protocols include initial user authentication, remote user authentication, Single Sign-On (SSO), secure sessions and file transfer, cloud transmission and key management protocols.
Acknowledgement
We would like to express our gratitude to Sri Panem Nadipi Chennaih for his assistance and encouragement during the process of writing this book chapter, which we have decided to dedicate to him.
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2The Role of Machine Learning and Artificial Intelligence in Detecting the Malicious Use of Cyber Space
Charanarur Panem1, Srinivasa Rao Gundu2* and J. Vijaylaxmi3
1School of Cyber Security and Digital Forensic, National Forensic Sciences University, Goa Campus, Goa, India
2Department of Computer Science, Government Degree College-Sitaphalmandi, Hyderabad, Telangana, India
3PVKK Degree & PG College, Anantapur, Andhra Pradesh, India
Abstract
Cyber security has become a major cause of concern in today’s digital world. Data breaches, identity theft, captcha decoding, and other situations that affect millions of individuals and businesses are all too common. When it comes to cyber attacks and crimes, it has always been tough to come up with effective rules and procedures and then put them into action with pinpoint precision. Because of recent developments in artificial intelligence, cyber attacks and criminal behaviour are becoming more common. Research and engineering, including medicine, have all benefited from its utilization.
Artificial intelligence has ushered in a new age in everything from healthcare to robots. When hackers couldn’t resist this hot commodity, they turned what were formerly “regular” computer attacks into more “intelligent” forms of crime.
The authors of this chapter examine a variety of AI techniques that they feel have significant promise. Among other things, they go through how to use these tactics in cyber security. They wrap off their discussion with a discussion on the future uses of artificial intelligence and cyber defense.
Keywords: Cyber security, digital world, identity theft, captcha, artificial intelligence, cyber attacks, robots, hackers
2.1 Introduction
To a large extent, hacking has developed from simple theft or vandalism to well-organized and financially sponsored criminals that want to benefit on a global scale because of fast technological advancement. In this case, organized crime’s aims might range from personal gain to political development.
As the world becomes more reliant on technology, businesses of all kinds must act faster than ever to protect themselves against cyber assaults and criminal activity. Predicting and detecting an attack before it happens is one of the most critical and hardest parts of Cybersecurity. Cyber assaults may take a number of forms, with varying degrees of sophistication, scope, and objectives. Because of the vast range of threats, organizations and governments must prioritize cyber security as a top priority [1].
