Conversational Artificial Intelligence E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1676

Veröffentlichungsjahr: 2024

Beschreibung

CONVERSATIONAL ARTIFICIAL INTELLIGENCE This book reviews present state-of-the-art research related to the security of cloud computing including developments in conversational AI applications. It is particularly suited for those that bridge the academic world and industry, allowing readers to understand the security concerns in advanced security solutions for conversational AI in the cloud platform domain by reviewing present and evolving security solutions, their limitations, and future research directions. Conversational AI combines natural language processing (NLP) with traditional software like chatbots, voice assistants, or an interactive voice recognition system to help customers through either a spoken or typed interface. Conversational chatbots that respond to questions promptly and accurately to help customers are a fascinating development since they make the customer service industry somewhat self-sufficient. A well-automated chatbot can decimate staffing needs, but creating one is a time-consuming process. Voice recognition technologies are becoming more critical as AI assistants like Alexa become more popular. Chatbots in the corporate world have advanced technical connections with clients thanks to improvements in artificial intelligence. However, these chatbots' increased access to sensitive information has raised serious security concerns. Threats are one-time events such as malware and DDOS (Distributed Denial of Service) assaults. Targeted strikes on companies are familiar and frequently lock workers out. User privacy violations are becoming more common, emphasizing the dangers of employing chatbots. Vulnerabilities are systemic problems that enable thieves to break in. Vulnerabilities allow threats to enter the system, hence they are inextricably linked. Malicious chatbots are widely used to spam and advertise in chat rooms by imitating human behavior and discussions, or to trick individuals into disclosing personal information like bank account details.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 A Glance View on Cloud Infrastructures Security and Solutions

1.1 Introduction

1.2 Methodology

1.3 Literature Review

1.4 Open Challenges

1.5 Recommendations

1.6 Conclusion

Acknowledgments

References

2 Artificial Intelligence Effectiveness for Conversational Agents in Healthcare Security

2.1 Introduction

2.2 Types of AI Relevance to Healthcare

2.3 The Future of AI in Healthcare

2.4 Ways of Artificial Intelligence that Will Impact Hearlthcare

2.5 AI Models

2.6 Compare E-Cohort Findings on Wearables and AI in Healthcare

2.7 Ethical Concerns of AI in Healthcare

2.8 Future in Healthcare

2.9 Conclusion

References

3 Conversational AI: Security Features, Applications, and Future Scope at Cloud Platform

3.1 Introduction

3.2 How Does Conversational Artificial Intelligence (AI) Work?

3.3 The Conversational AI Components

3.4 Uses of Conversational AI

3.5 Advantages of Conversational AI

3.6 Challenges with Conversational Artificial Intelligence

3.7 Risks Associated with Conversational AI

3.8 Proposed Model for Conversational AI in Cloud Platform

3.9 Conclusion

3.10 Future Work

References

4 Unsupervised BERT-Based Granular Sentiment Analysis of Literary Work

4.1 Introduction

4.2 Related Works

4.3 Text Extraction

4.4 Data Preprocessing

4.5 Sentiment Analysis on Literary Works

4.6 TF-IDF Vectorizer

4.7 Fine-Grained Sentiment Analysis on Literary Data

4.8 BERT Classifier for Unsupervised Learning

4.9 Conclusion

References

5 Extracting and Analyzing Factors to Identify the Malicious Conversational AI Bots on Twitter

5.1 Introduction

5.2 Literature Review

5.3 Methods

5.4 Results and Discussion

5.5 Conclusion and Future Direction

References

6 Evolution and Adoption of Conversational Artificial Intelligence in the Banking Industry

6.1 Introduction

6.2 Significance of Artificial Intelligence

6.3 Conversational AI in the Indian Banking Industry

6.4 Conversational AI in Use in Various Companies

6.5 Conclusion

References

7 Chatbots: Meaning, History, Vulnerabilities, and Possible Defense

7.1 Understanding Chatbots

7.2 History of Chatbots

7.3 Vulnerabilities and Security Concerns of Chatbots

7.4 Possible Defense Strategies

7.5 Conclusion

References

8 Conversational Chatbot-Based Security Threats for Business and Educational Platforms and Their Counter Measures

8.1 Introduction

8.2 Chatbot Applications in Education, Business Management, and Health Sector

8.3 Security and Privacy in Chatbot

8.4 Related Work

8.5 Methodology

8.6 Results and Discussion

8.7 Conclusion

References

9 Identification of User Preference Using Human–Computer Interaction Technologies and Design of Customized Reporting for Business Analytics Using Ranking Consistency Index

9.1 Introduction

9.2 Literature Review

9.3 Design of Metric for Ranking Consistency Index

9.4 Experimentation

9.5 Results and Discussion

9.6 Conclusion

References

10 Machine Learning for Automatic Speech Recognition

10.1 Introduction

10.2 Related Work

10.3 Methodology

10.4 Results

10.5 Conclusion

References

11 Conversational Artificial Intelligence at Industrial Internet of Things

11.1 Introduction

11.2 Technology Components Used in Conversational AI

11.3 Benefits of Conversational AI

11.4 How to Create Conversational AI?

11.5 Conversational Platforms and Internet of Things: Relevance and Benefits

11.6 Internet of Things Status for Industry

11.7 Scope of IIoT in Future

11.8 Work of IIoT with Additional New Innovations

11.9 Conclusion

References

12 Performance Analysis of Cloud Hypervisor Using Network Package Workloads in Virtualization

12.1 Introduction

12.2 A Related Study on Energy Efficiency

12.3 Motivation

12.4 Experiment Methodology and Setup

12.5 Results and Discussion

12.6 Conclusion

References

13 Evaluation of Chabot Text Classification Using Machine Learning

13.1 Introduction

13.2 Literature Survey

13.3 Methodology

13.4 Results

13.5 Conclusion

References

14 Enhanced Security in Chatbot

14.1 Introduction

14.2 Architecture of Chatbots

14.3 Working of Chatbots

14.4 Background

14.5 Literature Survey

14.6 Proposed System

14.7 Analysis of the Work

14.8 Future Work

14.9 Conclusion

References

15 Heart Disease Prediction Using Ensemble Feature Selection Method and Machine Learning Classification Algorithms

15.1 Introduction

15.2 Review of Literature

15.3 Proposed Methodology

15.4 Experimental Results

15.5 Conclusion

References

16 Conversational AI: Dialoguing Most Humanly With Non-Humans

16.1 Introduction

16.2 History

16.3 Chatbot vs. Conversational AI

16.4 Dialogue Systems

16.5 Human Computer Interaction

16.6 Artificial Intelligence

16.7 Components of Conversational AI

16.8 Frameworks, Models, and Architectures

16.9 Conclusion

References

17 Counterfeit Pharmaceutical Drug Identification

17.1 Introduction

17.2 Materials and Methods

17.3 Results and Discussion

17.4 Conclusion

References

18 Advanced Security Solutions for Conversational AI

18.1 Introduction

18.2 Background

18.3 Components of Conversational AI

18.4 Challenges with Conversational AI

18.5 Conclusion

References

19 Security Threats and Security Testing for Chatbots

19.1 Introduction

19.2 Related Work

19.3 Vulnerability Assessment Tools

19.4 Penetration Testing

19.5 Vulnerabilities in Chatbot

19.6 Conclusion

References

20 ChatBot-Based Next-Generation Intrusion Detection System

20.1 Introduction

20.2 Literature Survey

20.3 Methodology

20.4 Result Analysis

20.5 Conclusion

References

21 Conversational Chatbot With Object Recognition Using Deep Learning and Machine Learning

21.1 Introduction

21.2 Literature Survey

21.3 Methodology

21.4 Results and Discussion

21.5 Conclusion

References

22 Automatic Speech Recognition Design Modeling

22.1 Introduction

22.2 Literature Survey

22.3 Methodology

22.4 Experimental Result Analysis

22.5 Conclusion

References

23 The Future of Modern Transportation for Smart Cities Using Trackless Tram Networks

23.1 Introduction

23.2 Proposed System Architecture

23.3 Working Process of the TRAM-RSU Framework

23.4 Experimental Analysis

23.5 Summary

References

24 Evaluating the Performance of Conversational AI Tools: A Comparative Analysis

24.1 Introduction

24.2 Literature Review

24.3 Methodology

24.4 Result

24.5 Discussion

24.6 Conclusion

References

25 Conversational AI Applications in Ed-Tech Industry: An Analysis of Its Impact and Potential in Education

25.1 Introduction

25.2 Conversational AI in Ed-Tech Overview

25.3 Methodology

25.4 Conclusion, Limitation, and Future Work

References

26 Conversational AI: Introduction to Chatbot’s Security Risks, Their Probable Solutions, and the Best Practices to Follow

26.1 Introduction

26.2 Related Work

26.3 History and Evolution of Chatbots

26.4 Components & Concepts that Make Conversational AI Possible

26.5 Working of Conversational AI

26.6 Reasons Behind why Companies are Using Chatbot

26.7 Plans for the Future Development of Conversational AI

26.8 Security Risks of Conversational AI’s Chatbot

26.9 Probable Solutions to the Security Vulnerabilities

26.10 Privacy Laws for the Security of Conversational AI and Chatbot

26.11 Chatbot and GDPR

26.12 Best Practices to Follow to Ensure Chatbot Security

26.13 Conclusion

Acknowledgment

References

27 Recent Trends in Pattern Recognition, Challenges and Opportunities

27.1 Introduction

27.2 Optical Character Recognition

27.3 Various Sectors of Pattern Recognition

27.4 Applications of Natural Language Processing

27.5 Conclusion

References

28 A Review of Renewable Energy Efficiency Technologies Toward Conversational AI

28.1 Introduction

28.2 Renewable Energy

28.3 Energy Technologies

28.4 Conversational AI

28.5 Conclusion

References

29 Messaging Apps Vulnerability Assessment Using Conversational AI

29.1 Introduction

29.2 Penetration Test

29.3 Mobile App Security

29.4 Discovered Vulnerabilities in Mobile Applications

29.5 Mitigation Strategies Against Cross-Site Scripting and SQL Attacks

29.6 Mobile Application Security Framework

29.7 Conclusion

References

30 Conversational AI Threat Identification at Industrial Internet of Things

30.1 Introduction

30.2 IoT Layered Architecture

30.3 Security Issues in IoT

30.4 Literature Survey of Various Attacks on Industrial Internet of Things

30.5 Various Attacks in Industrial Internet of Things

30.6 Recent Attacks on Industrial IoT

30.7 Conclusion

References

31 Conversational AI—A State-of-the-Art Review

31.1 Introduction

31.2 Related Work

31.3 Problem Statement

31.4 Proposed Methodology

31.5 Regulatory Landscape of Conversational AI

31.6 Future Works

31.7 Conclusion

References

32 Risks for Conversational AI Security

32.1 Introduction

32.2 Related Work

32.3 History and Evolution of Conversational AI Security

32.4 Components and Concepts that Make Coversational AI Security

32.5 Working of Conversational AI Security

32.6 Risk for Conversational AI Security

32.7 Solutions for Conversational AI Security

32.8 Conclusion

Acknowledgement

References

33 Artificial Intelligence for Financial Inclusion in India

33.1 Introduction

33.2 Digitalization of Banking Sector Paving Way for AI in Financial Inclusion

33.3 Technology Acceptance Model

33.4 AI and Use of AI in Financial Inclusion

33.5 Conclusion

References

34 Revolutionizing Government Operations: The Impact of Artificial Intelligence in Public Administration

34.1 Introduction

34.2 Methodology

34.3 The Origin and Development of AI Technology and Current Methodologies in the Field

34.4 Artificial Intelligence in Indian Governance

34.5 Discharge of Government Functions

34.6 Challenges

34.7 Conclusion

References

Bibliography

35 Conversational AI and Cloud Platform: An Investigation of Security and Privacy

35.1 Introduction

35.2 Cloud Architecture

35.3 Literature Survey

35.4 Security in Conversational AI and Cloud Computing

35.5 Conclusion

References

36 Chatbot vs Intelligent Virtual Assistance (IVA)

36.1 Introduction

36.2 Related Work [13]

36.3 Problem Statement

36.4 Proposed Methodology

36.5 Regulatory Landscape

36.6 Future Works

36.7 Conclusion

References

37 Digital Forensics with Emerging Technologies: Vision and Research Potential for Future

37.1 Introduction

37.2 Background Work

37.3 Digital Twin Technology—An Era of Emerging Technology

37.4 Security

37.5 Digital Forensics Characteristics

37.6 Computer Forensics

37.7 Tool Required for Digital Forensics

37.8 Importance of Computer and Digital Forensics in Smart Era

37.9 Methods/Algorithms for Digital Forensics in Smart Era

37.10 Popular Tools Available for Digital Forensics

37.11 Popular Issues Towards Using AI–Blockchain–IoT in Digital Forensics

37.12 Future Research Opportunities Using AI-Blockchain-IoT in Digital Forensics

37.13 Limitations AI/ML–Blockchain–IoT-Based Smart Devices in Digital Forensics

37.14 Conclusion

References

38 Leveraging Natural Language Processing in Conversational AI Agents to Improve Healthcare Security

38.1 Introduction

38.2 Natural Language Process in Healthcare

38.3 Role of Conversational AI in Healthcare

38.4 NLP-Driven Security Measures

38.5 Integrating NLP With Security Framework

38.6 Conclusion

References

39 NLP-Driven Chatbots: Applications and Implications in Conversational AI

39.1 Introduction

39.2 Related Work

39.3 NLP-Driven Chatbot Technologies

39.4 Chatbot Software for Automated Systems

39.5 Conclusion

References

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Comparing E-cohort findings on wearables and AI in healthcare.

Chapter 3

Table 3.1 Risks associated with conversational AI [10].

Table 3.2 Attacks that use distributed denial of service (DDoS).

Chapter 5

Table 5.1 Top 12 common independent features.

Chapter 6

Table 6.1 Usage of artificial intelligence in the financial industry categoriz...

Chapter 8

Table 8.1 Features selected using the CFS algorithm.

Chapter 9

Table 9.1 List of selected alternatives (users).

Table 9.2 List of criteria (financial ratios).

Table 9.3 Ranking Consistency Index Metric (M

RCI

) for normalization techniques...

Table 9.4 Normalization techniques and ranks for decision matrix of size 4*4, ...

Table 9.5 Calculation of total number of times same and different ranking prod...

Table 9.6 Ranking of alternatives with GFTOPSIS (LM-N) and GFTOPSIS (LMM-N).

Chapter 12

Table 12.1 Windows VM.

Table 12.2 Ubuntu VM.

Table 12.3 An algorithm for details on the graph algorithm.

Table 12.4 Type 1 hypervisors of cloud platform.

Chapter 14

Table 14.1 Generation of hash code.

Table 14.2 Parameters used in the work.

Chapter 15

Table 15.1 Dataset features and description.

Table 15.2 Pseudo-code for frequent feature subset selection method.

Table 15.3 Frequency count of each feature.

Table 15.4 Survey of existing and proposed models.

Chapter 16

Table 16.1 History of conversational agents.

Table 16.2 Chatbot vs conversational AI.

Table 16.3 Comparison of Rasa NLU and Rasa core.

Chapter 17

Table 17.1 Tesseract accuracy metrics.

Table 17.2 SpaCy accuracy metrics.

Table 17.3 Comparison of NER accuracy with existing models.

Chapter 23

Table 23.1 Classification types of vehicular nodes.

Table 23.2 List of notations used in the TRAM-RSU framework.

Table 23.3 List of pseudocode 1 notations.

Table 23.4 List of pseudocode 2 notations.

Table 23.5 Existing schemes.

Chapter 24

Table 24.1 Comparison of chatbots/virtual assistant/conversational agent.

Table 24.2 Feature comparison of the smart invigilation tools used in educatio...

Chapter 25

Table 25.1 Search query.

Table 25.2 Database search.

Table 25.3 Quality evaluation criteria.

Table 25.4 Selected article relevance with research question.

Table 25.5 Quality evaluation result.

Table 25.6 Details of platforms used for conversational AI.

Chapter 31

Table 31.1 Literature review of existing techniques.

Chapter 33

Table 33.1 Basic Saving Banking Deposit account (BSBD account).

Table 33.2 Top reasons banks use Artificial Intelligence.

Chapter 36

Table 36.1 Technique comparisons.

List of Illustrations

Chapter 2

Figure 2.1 Multilayered feed-forward artificial neuron network.

Figure 2.2 Azure confidential computing architecture.

Figure 2.3 Artificial Intelligence in Healthcare Market Size Report, 2030.

Chapter 3

Figure 3.1 Cloud computing architecture.

Figure 3.2 The general dialogue system’s structure.

Figure 3.3 The structure of the next-generation of virtual personal assistants...

Figure 3.4 Conversational AI in the cloud platform model.

Chapter 4

Figure 4.1 Shakespeare’s Hamlet play dataset.

Figure 4.2 Text preprocessing.

Figure 4.3 Text sentiment.

Figure 4.4 Polarity-based sentiment analysis.

Figure 4.5 TF-IDF vectorization.

Figure 4.6 Char_count positive and negative sentiment.

Figure 4.7 Literary char_count.

Figure 4.8 Violin plot of sentiment.

Figure 4.9 Literary word cloud.

Figure 4.10 Accuracy of the BERT classifier on literary work.

Chapter 5

Figure 5.1 Variable/feature description.

Figure 5.2 Experiment 1—Feature selection without outliers and extreme value t...

Figure 5.3 Experiment 2—Feature selection with outliers and extreme value trea...

Chapter 7

Figure 7.1 Example of a rule-based chatbot.

Figure 7.2 Example of an AI chatbot.

Figure 7.3 Timeline of some of the most popular chatbots.

Chapter 8

Figure 8.1 Chatbot communication flow.

Figure 8.2 Chatbot components.

Figure 8.3 Security threats.

Figure 8.4 Machine learning techniques for design of intrusion detection syste...

Figure 8.5 Accuracy of machine learning techniques for classification and pred...

Figure 8.6 Sensitivity of machine learning techniques for classification and p...

Figure 8.7 Specificity of machine learning techniques for classification and p...

Chapter 9

Figure 9.1 RCI metric for normalization techniques.

N1

Vector Normalization,

N

...

Figure 9.2 User preference on MCR with GFTOPSIS (LM-N) and GFTOPSIS (LMM-N).

Chapter 10

Figure 10.1 Deep learning in speech recognition.

Figure 10.2 Machine learning for automatic speech recognition.

Figure 10.3 Accuracy of classifiers for automatic speech recognition.

Figure 10.4 Sensitivity of classifiers for automatic speech recognition.

Figure 10.5 Specificity of classifiers for automatic speech recognition.

Figure 10.6 Precision of classifiers for automatic speech recognition.

Figure 10.7 Recall of classifiers for automatic speech recognition.

Figure 10.8 Accuracy, specificity, sensitivity, precision, and recall of class...

Chapter 12

Figure 12.1 The cloud computing stack.

Figure 12.2 Hyper-V architecture.

Figure 12.3 Cloud data center view; NAS stands for network area storage.

Figure 12.4 Completion times of varying computation-intensive workloads.

Figure 12.5 Overall comparison of VM.

Chapter 13

Figure 13.1 Block diagram of chatbot text classification.

Figure 13.2 Machine learning for chatbot text classification.

Figure 13.3 Accuracy comparison of classifiers for chatbot text classification...

Figure 13.4 Precision comparison of classifiers for chatbot text classificatio...

Figure 13.5 Recall comparison of classifiers for chatbot text classification.

Figure 13.6 F1 score comparison of classifiers for chatbot text classification...

Chapter 14

Figure 14.1 Layers of the architecture of a smart campus and its impact on the...

Figure 14.2 Conceptual framework of behavioral activation in an AI-based chatb...

Figure 14.3 System model (DM, Y-TH, & HD, 2021).

Figure 14.4 Comparison of security in both systems.

Chapter 15

Figure 15.1 Proposed model of HD prediction.

Figure 15.2 Comparison accuracy of three models.

Figure 15.3 Confusion matrix, classification report, and ROC curve.

Chapter 16

Figure 16.1 Dialogue systems.

Figure 16.2 Relation between AI, linguistics, ML, DL, NLP, NLU, and NLG.

Figure 16.3 Components of conversational AI.

Figure 16.4 HLD of intent classification in Rasa.

Figure 16.5 HLD of intent and entity identification.

Figure 16.6 HLD of Rasa architecture.

Figure 16.7 HLD of RLHF.

Chapter 17

Figure 17.1 WHO statistics of counterfeit drug selling based on categories.

Figure 17.2 Workflow diagram of proposed system for pharmaceutical drug classi...

Figure 17.3 Input back-strip image before preprocessing.

Figure 17.4 Input back-strip image after preprocessing.

Figure 17.5 NER mechanism to identify drug names.

Figure 17.6 Hashing database search mechanism.

Figure 17.7 User interface of an application.

Chapter 18

Figure 18.1 Functionality of a chatbot system.

Figure 18.2 Chatbot security specification.

Chapter 19

Figure 19.1 Accunetix scanner.

Figure 19.2 OWASP scanner.

Figure 19.3 Burpsuite vulnerability scanner.

Figure 19.4 Vulnerabilities in chatbot.

Chapter 20

Figure 20.1 Chatbot-based next generation intrusion detection system.

Figure 20.2 NSL KDD dataset distribution.

Figure 20.3 Accuracy of classifiers for chatbot IDS.

Figure 20.4 Sensitivity of classifiers for chatbot IDS.

Figure 20.5 Specificity of classifiers for chatbot IDS.

Figure 20.6 Precision of classifiers for chatbot IDS.

Figure 20.7 Recall of classifiers for chatbot IDS.

Chapter 21

Figure 21.1 Convolutional neural network.

Figure 21.2 Accuracy of classifiers for object recognition using a chatbot.

Figure 21.3 Sensitivity of classifiers for object recognition using a chatbot.

Figure 21.4 Specificity of classifiers for object recognition using a chatbot.

Figure 21.5 Precision of classifiers for object recognition using a chatbot.

Figure 21.6 Recall of classifiers for object recognition using a chatbot.

Chapter 22

Figure 22.1 Automatic speech recognition using feature selection and machine l...

Figure 22.2 Comparison of machine learning algorithm for automatic speech reco...

Chapter 23

Figure 23.1 Proposed application scenario.

Figure 23.2 Internal modules of the single TRAM-RSU framework.

Figure 23.3 Network flow sub-system.

Figure 23.4 CBM transmission for connection request.

Figure 23.5 Network connectivity level analysis.

Figure 23.6 Network load analysis.

Figure 23.7 Average server utilization.

Figure 23.8 Average processing time analysis.

Chapter 24

Figure 24.1 Methodology phases.

Figure 24.2 Types of evaluation metrics.

Figure 24.3 Comparison of the platforms used for implementing conversational A...

Figure 24.4 Conduct of a research activity.

Chapter 25

Figure 25.1 Timeline of digital education transformation.

Figure 25.2 Types of conversational AI in education.

Figure 25.3 Activity composition of teachers’ working hours.

Figure 25.4 Adoption of AI-based systems in Education [2].

Figure 25.5 Percentage of various platforms of CAI used in education domains.

Figure 25.6 Role of conversational AI in education.

Figure 25.7 Benefits of conversational AI.

Chapter 27

Figure 27.1 Pattern recognition.

Figure 27.2 Pattern recognition processing.

Figure 27.3 Applications of pattern recognition.

Chapter 29

Figure 29.1 Vulnerabilities discovered, ranked according to their degree of se...

Figure 29.2 Mobile application security framework.

Figure 29.3 Risk level in mobile applications.

Chapter 30

Figure 30.1 IoT architecture.

Chapter 31

Figure 31.1 Natural language question flow.

Figure 31.2 Natural language processing paradigm.

Figure 31.3 Chatbot processing paradigm.

Chapter 33

Figure 33.1 Source: 2021–22 Trend Report on Financial Inclusion; Bankers Insti...

Figure 33.2 Technology acceptance model (TAM).

Figure 33.3 Aadhaar E-KYC API Specification-Version 2.0; source—UIDAI-May 2016...

Chapter 35

Figure 35.1 Working of Chatbot.

Figure 35.2 Cloud computing architecture.

Figure 35.3 Cloud security report by BitGlass Agency.

Chapter 36

Figure 36.1 There is a variety of functions that you can use in a chatbot.

Figure 36.2 A rule-based chatbot working by a set of predefined rules.

Chapter 37

Figure 37.1 The taxonomy for machine learning adoption in blockchain-based sma...

Chapter 38

Figure 38.1 Applications of NLP [1, 2].

Figure 38.2 NLP process in healthcare.

Figure 38.3 Applications of conversational AI in healthcare.

Figure 38.4 Architecture of NLP-data-driven architecture.

Figure 38.5 Integration of NLP tasks.

Chapter 39

Figure 39.1 Conversational AI-based process flow for chatbots.

Figure 39.2 Architecture of a chatbot.

Figure 39.3 Chatbot framework based on conversations.

Figure 39.4 General architecture of NLP-driven chatbots.

Figure 39.5 Working process of NLP-based chatbots.

Figure 39.6 Process of chatbot software for an automated system.

Figure 39.7 Future trends of a NLP-driven chatbot software.

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])

Conversational Artificial Intelligence

Edited by

Romil RawatRajesh Kumar ChakrawartiSanjaya Kumar SarangiAnand RajavatMary Sowjanya AlamandaKotagiri Srividya

and

Krishnan Sakthidasan Sankaran

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and ScrivenerPublishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 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 9781394200566

Cover images: Machine Learning AI: Semisatch | Dreamstime.com, AI Chess: Maxuser2 | Dreamstime.com, AI MonitoringMachineL Ekkasit919 | Dreamstime.comCover design by Kris Hackerott

Preface

The book talks about how conversational artificial intelligence (AI) models, like chatbots, may help IT departments work more efficiently by offering solutions like self-service chatbots that let users change their passwords and do other user identification procedures. The use of chatbots, or artificial conversation systems, is growing, although not all of their security issues have been resolved. The term “conversational AI” refers to a group of technologies that allow computers and other devices to create speech-and automated messaging applications. This enables human-like interaction between humans and robots. One of the many chatbots available today with speech recognition and the ability to respond to challenging inquiries is Alexa. As demand for AI assistants rises, voice recognition technologies are becoming increasingly crucial. Hackers and other harmful software are naturally drawn to chatbot technology since it is responsible for obtaining and safeguarding sensitive information. Businesses have included conversational chatbots and automatic response software on their websites and social media platforms despite the increased threat of cyberattacks. Platforms like Facebook, WhatsApp, and WeChat frequently deploy chatbots in their customer service operations. While conversational AI will probably improve company processes, hackers may also use it to reroute their cyberattacks. AI systems already possess a plethora of knowledge about people, which helps them better grasp the kinds of arguments that each individual respond to, When coupled with extraordinary human-like conversational abilities, it is a recipe for disaster. Possible results include phishing attacks, spam calls, and fraudulent endeavours. In order to serve clients via a spoken or written interface, conversational AI integrates natural language processing with conventional software such as chatbots, voice assistants, or an interactive speech recognition system. Customers are helped by conversational chatbots, which are an intriguing development since they make the customer service sector relatively self-sufficient and react to their inquiries quickly and accurately. A well-automated chatbot may drastically reduce personnel demands, but building one takes time. The importance of voice recognition systems is increasing as AI assistants like Alexa gain popularity. Thanks to advancements in artificial intelligence, chatbots in the business sphere may now communicate with clients in a sophisticated and technical way. Contrary to popular belief, the proliferation of sensitive data in these chatbots has led to grave security problems. Threats are one-off occurrences like malware or DDOS attacks.

1A Glance View on Cloud Infrastructures Security and Solutions

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

Clients may benefit from cutting-edge cloud computing solutions created and offered in a cost-effective way by firms. In terms of cloud computing, the most serious problem is security, which serves as a significant disincentive to individuals from embracing the technology in the first place. Making cloud computing secure, particularly when it comes to the underlying infrastructure, is essential. The domain of cloud infrastructure security has been subjected to a number of different research programs; nonetheless, certain gaps remain unresolved, and new challenges continue to emerge. This article provides an in-depth analysis of security issues that might arise at various levels of the cloud architecture hierarchy. Specifically, it focuses on the most significant infrastructure-related challenges that might have an impact on the cloud computing business model in the near future.

This chapter also discusses the several literature-based approaches to dealing with the different security challenges at each level that are now accessible. To assist in the resolution of the challenges, a list of the obstacles that have still to be conquered is presented. It has been discovered that numerous cloud characteristics such as flexibility, elasticity, and multi-tenancy create new problems at each infrastructure level after conducting an examination of the existing challenges. According to research, a variety of security threats, including lack of availability, unauthorized usage, data loss, and privacy violations, have the greatest effect across all levels of infrastructure. Multi-tenancy, in particular, has been proven to have the largest effect on infrastructure at all levels, even the most basic. The study comes to a close with a number of suggestions for further research.

Keywords: Cloud computing, secure cloud infrastructure, application security, network security, host security, data security

1.1 Introduction

Models for offering cloud computing services include the ones listed below as examples:

When it comes to providing cloud services, there are three fundamental models to consider, each of which is becoming more established and common with each passing generation. For this, there are many various approaches to consider, including software as a service, platform as a service, and infrastructure as a service (to name a few). A few of these strategies include software development, platform and infrastructure as a service, and cloud computing, among others (IaaS). In contrast to these three major models [1], an SPI model is a combination of them and may be characterized as follows:

In order to get access to programs that are hosted on service provider infrastructure, users must connect to them over the Internet. This is referred to as software as a service (also known as SaaS for short) or cloud computing, depending on who you ask. These strategies assist the customers of software offered under the SaaS business model, who are typically end users who subscribe to readily available programs. The SaaS model has also been associated with a pay and use feature that would allow the end users to access software through a web browser without having to deal with the headaches of installation, maintenance, or making a significant upfront payment [2]. Some of the popular SaaS apps include Sales force, Google Apps, and Google Docs.

User awareness is an important component of SaaS security from a security viewpoint. However, the SaaS provider must hold on to a set of security conditions in order to ensure that users adhere to the essential security protocols while using the service. Things like multi-factor authentication, complicated passwords, and password retention are examples of these requirements. An additional component that SaaS providers should have in place is the adoption of security measures to secure customers’ data and to guarantee that it is available for permitted usage at all times [3].

In computing, the phrase Platform as a Service refers to a collection of software and development tools that are stored on the servers of a service provider and are available from any location on the Internet. It provides developers with a platform on which they may construct their apps without having to worry about the underlying mechanics of the service they are relying on for support. It also makes it easier to manage the software development life cycle, from planning to maintenance, in an efficient and effective way, thanks to the PaaS architecture.

The platform also makes use of programming languages such as VC++, Python, Java, etc. to allow users to construct their own apps on top of it. Many developers and programmers now depend on Platform as a Service (PaaS) firms such as WordPress, Go Daddy, and Amazon Web Services to build their websites and host their online applications. Security, according to the PaaS paradigm, is a shared responsibility that must be handled by both developers and service providers in equal measure. Example: When developing applications, developers must follow security standards and best practices to guarantee that the applications are safe and secure. A programmer, for example, must certify that the software is free of flaws and vulnerabilities [4] before exposing it to the general public.

Aspects of this process that are equally important include the detection and correction of any security flaws that attackers may exploit in order to get access to and compromise users’ data. For developers, the dependability of PaaS technology, on the other hand, is critical in order to provide a safe and secure environment for application development. For example, several programming environments, such as C++, are well-known for having poor memory management, which enables attackers to conduct a variety of assaults against their victims, including stack overflows.

A lack of sufficient authentication in some relational database management systems (RDBMSs), such as Oracle, may also be exploited by attackers. Oracle, for example, allows users who have been granted admin permissions at the operating system level to access the database without the need for a username and password [5].

A kind of cloud computing paradigm in which a cloud computing service provider keeps the resources that are only shared with contractual customers that pay a per-use charge to the cloud computing service provider is known as Infrastructure as a Service (IaaS). In particular, one of the key benefits of the Equipment as a Service model is that it removes the need for a significant initial investment in computer infrastructure such as networking devices, computer processors and storage capacity, and servers. The technology may also be used to quickly and cost-effectively increase or reduce the amount of computer resources available to a user. In this day and age, with the proliferation of cloud delivery systems, it may be challenging to determine the boundaries of one’s security responsibilities. Security is the responsibility of both cloud service providers (CSPs) and the clients that use their services. As seen in Illustration 5, the duties of cloud computing service delivery models are outlined. Cloud computing services include infrastructure as a service (IaaS) offerings such as Amazon Web Services, Cisco Meta-cloud, Microsoft Azure, and Google Compute Engine (GCE). It is important to note that customer-facing infrastructure is critical in terms of security since it acts as the first line of defense for the system’s perimeter.

In this environment, attackers may use a variety of strategies to target the infrastructure, including denial of service (DoS) attacks and malware distribution campaigns. The majority of the time, the security of a PaaS solution is the responsibility of the service provider.

Cloud Models and Architectures: An introduction determining the kind of cloud an institution should use is the first and most important stage in cloud deployment, as this will allow for a more smooth installation process to take place. During the cloud deployment process, the second and final step is known as deployment. According to the authors, institutions who have failed to execute a deployment plan have done so as a result of selecting the incorrect kind of cloud infrastructure. In order to prevent failure, organizations must first assess their data before deciding on the kind of cloud infrastructure to use. While many consumers consider security when signing up for cloud services, many do not because they have a misconception of the efficiency of the protection given by cloud services in and of itself. When it comes to keeping their data secure, many businesses that use cloud computing depend only on the security measures employed by cloud service providers. This may provide hostile actors the ability to exploit client-side vulnerabilities in order to attack the systems of one or more tenants as a result of the situation [6].

To mention a few examples, public cloud, private cloud, community cloud, and hybrid cloud are all concepts that are being explored.

Public cloud is often referred to as an external cloud in some areas, as is the case with the Amazon Web Services (AWS) public cloud. This kind of cloud is accessible to all users or large groups of users through the Internet, with cloud service providers retaining control over the environment. Customers may access any data that are made accessible on the network using this service, which is managed by the service provider. A cost-effective and scalable means of implementing information technology solutions is made feasible via the usage of public cloud computing. Because of the Internet connection, a variety of security dangers are introduced into the system, including denial-of-service (DoS) attacks, malware, ransomware, and advanced persistent threat (APT) assaults [7, 8].

Cloud inside an organization: The private cloud, also known as the internal cloud, is a kind of cloud that is used within an organization. This category’s emphasis is focused on a single user, group, or institution at the time of writing. Although the cost of private clouds is more than the cost of public clouds, they are more secure than public clouds. The fact that a private cloud is housed behind an enterprise’s firewall allows users within the organization to access it via the company’s intranet. Privatized clouds, in contrast to public cloud computing, are less secure since less money and experience is directed on the development of services and systems, much alone the protection of data in the private cloud. Consequently, some components may become vulnerable, allowing hostile actors to conduct attacks against these vulnerable components by exploiting the weaknesses of these vulnerable components [8].

The community cloud provides assistance for a variety of communities with common interests, such as missions, rules, security needs, and regulatory compliance difficulties, among other things. Depending on the circumstances, institutions or a third party may be in charge of managing it on-site or off-site. When compared to the standard cloud, the community cloud offers stronger privacy, security, and policy compliance protections. The degree of security in a community cloud environment is determined by the quantity of security awareness present in the community, as well as the importance of security to the activities of the community as a whole. The cloud storage of sensitive data from a government agency may endanger national security if the material is made available to the public, as has happened in the past. It follows as a result that security measures should be included in cloud computing environments [9].

Hybrid Cloud: Due to the diverse variety of needs that an institution has, this kind of cloud deployment is required. It combines two or more models in order to deliver cloudbased computing services (public, private, or community). Enterprises may use private clouds to store sensitive data or apps in a secure environment while hosting non-sensitive data or applications in a public cloud environment. Because of the federation of clouds with a diverse set of incompatible security measures, cloud hybridization, on the other hand, generates a host of security challenges. A consequence of this is that attackers uncover vulnerabilities in one or more clouds with the intent of getting access to the whole infrastructure.

1.2 Methodology

In this research, the results were gathered through a review of the available literature. How to Plan and Organize the Review Process: The following are the three sub-phases of this phase: acquiring the research goals, establishing the research questions, and choosing the search technique to be employed in the study are all included in this phase.

The Investigation’s Goals and Objectives

The following are the key aims of the research:

The goal of this project is to provide a new taxonomy for safe cloud architecture based on the current state-of-the-art literature.

To provide an in-depth review of a wide range of issues and solutions that are used in cloud infrastructure at various degrees of complexity.

To draw attention to the disadvantages and dangers of the presently available solutions with respect to the research challenges and upcoming possibilities.

Take a look at the following questions:

Accordingly, the research explores if it is possible to answer two critical issues, which are listed below, in order to fulfill the goals.

Answer Question 1: What are some of the most well-known challenges in cloud computing architecture, as well as the proposed remedies at different levels of abstraction?

In your opinion, what is the security dangers associated with cloud that might prevent it from being more extensively used?

Various Methods of Obtaining Information

Academia’s digital resources, such as the ACM Digital Library, Arxiv, and a few more relevant international conferences, were employed to pull related works for this research from a variety of academic digital resources. It was also possible to find relevant worldwide conferences via Springer, IEEE Explore, Science Direct, ACM Digital Library, Arxiv, and a few more relevant international conferences through other sources.

It is believed that they are adequate for covering the most recent and credible literature on cloud infrastructure challenges as well current security solutions, according to the study’s authors who conducted the research. In the period between 2011 and 2020, an extensive study of the literature was conducted. For the purpose of obtaining reliable search results, this research searched large libraries using a combination of various search phrases that were generated using a reduplicate technique in order to increase the number of relevant studies found in the results (optimal results). The terms “Application Security” and “Network Security” were also among the most frequently used. These keywords were used to split the study into various categories, which allowed researchers to connect the relevant studies with the proper cloud infrastructure tiers, which comprised application, network, and host tiers as well as data and data infrastructure tiers, among others. In order to accomplish this approach, it is required to collect keywords and topics from the abstracts of the studies that emphasize the contributions of the study [10] that are relevant to the research.

1.3 Literature Review

Over the course of the previous decade, a number of survey studies have been published in which the security risks connected with the cloud computing environment have been explored. When it comes to cloud security, the great majority of the information that has been evaluated has made a substantial contribution to the management of these problems. One such study looked at the most often found cloud security flaws and discovered a number of them. They also offered a number of additional solutions to security challenges that arise in cloud architecture, each of which was meant to be sensitive to the personal data of individual users. Data transfer through the cloud is subject to considerable security risks, according to a research done. Participants in this survey were provided practical advice on how to deal with potential dangers over the course of the survey. The results of a study included a taxonomy and survey of cloud services, which were organized by cloud infrastructure providers and revenue.

A service taxonomy was created, which encompasses themes such as computers, networking, databases, storage, analytics, and machine learning, among other things, as well as additional topics. Regarding functionality, the computing, networking, and storage services provided by all cloud suppliers are of a high quality, and they are commonly recognized as the backbone of the cloud computing architecture.

According to a survey, cloud computing firms face a number of security issues. The cloud client, the cloud service provider, and the owner of the data stored in the cloud were all involved in this process. An investigation of various communication and storage options in the crypto cloud was also conducted as part of the project. Researchers conducting studies into the causes and consequences of different cyberattacks have access to the most up-to-date information.

Many data protection issues that may develop in a multi-tenant cloud computing system were examined and solutions were provided in a study published by the researchers. While this poll focused more on data privacy than security, the prior survey was concerned with both concerns at once.

A research gave a full definition of cloud computing, as well as the many different levels of cloud architecture that can be found in the cloud computing environment. Part of the research included a comparison of three service models (including SaaS, PaaS, and IaaS), as well as three deployment methodologies, as part of the overall research design (private, public, and community). It was determined that both private and public clouds have information security needs; thus, the writers looked into it. A few of the most urgent difficulties and restrictions related with cloud computing in terms of security were also covered during this session.

According to a study published in the journal, one of the many different types of vulnerabilities that often occur in cloud computing systems is the inability to recognize the flaws. To this research, the author’s contribution consisted in the categorization of different sorts of threats in accordance with the accessibility of cloud-based service resources. It was necessary to create this category in response to the extensive description and extent of the multiple dangers that were faced.

There are several concerns about the security of cloud computing infrastructure. Four critical levels of consideration should be taken into account while designing and executing cloud infrastructure security: the data level, the application level, the network level, and the host level (or the host itself) (or the physical location of the cloud infrastructure).

First and foremost, security refers to the protection of programs when they are using hardware and software resources in order to prevent others from gaining control of them. Among the most serious dangers at this level are distributed denial of service (DDoS) assaults on software programs, which are becoming more common.

Second, network-level security is concerned with network protection via the use of a virtual firewall, the creation of a demilitarized zone (DMZ), and data in transit protection procedures. Information about various kinds of firewalls should be monitored, collated, and preserved for future reference in order to achieve this goal.

Third, the degree of security refers to the protection that is offered for the host itself rather than for the virtual machine when a virtual server, hypervisor, or virtual machine is used in conjunction with another virtual machine. Obtaining information from system log files is required for the purpose of knowing when and where applications have been recorded in order to make these determinations. When it comes to defending cloud infrastructure, it is critical to look at the primary CIA components at each level of the organizational hierarchy. As cloud-based systems gain in popularity, the security dangers connected with their use are becoming better recognized. However, despite its many benefits, cloud computing is susceptible to a broad variety of security risks and assaults. The cloud computing infrastructure is always under assault, and attackers are constantly on the search for security flaws. The parts that follow discuss security issues that might arise at various levels of cloud architecture, as well as how to solve them.

Fourth is data-level difficulties: At this level of complexity, issues such as data breaches, data loss, data segregation, virtualization, confidentiality, integrity, and availability may all be discovered.

In terms of application-specific options, there are a plethora of options accessible.

The authors have presented an ECC-based multi-server authentication approach that is specific to the MCC context and does not need any pairing on the part of the users. While saving time and money, this method also maintains the benefits of more expensive pairing systems, such as safe mutual authentication, anonymity, and scalability, without necessitating the use of extra resources. This is shown theoretically by the formal security model, which illustrates the robustness of the method in practice.

The Open Stack platform was used as a reference by the authors in order to develop a number of models for information and resource sharing among tenants in an IaaS cloud environment, which were then evaluated. A tenant is encouraged to interact with the IT resources of other tenants in a regulated manner by using the models provided. Network access to virtual machines (VMs) must be regulated, however, in order to prevent malicious software from moving data in an uncontrolled way from the virtual machine.

According to the results, unique access control architecture for cloud computing that addresses cloud security and privacy challenges has been created. In order to construct the suggested system, the notion of dynamic trustworthiness served as its foundation. An access control system based on dynamic trustworthiness is used to, among other things, minimize the probability of undesirable behavior and ensure that only authorized users have access to cloud resources. The results reveal that the system recognizes potentially dangerous actions in order to prevent unlawful access, which would improve cloud computing security and, as a consequence, raise user confidence in the system, according to the researchers.

A hybrid access control framework, called iHAC, was presented by the authors, which allows type enforcement and role-based access control to be utilized in combination with other access control techniques. As a result, the architecture recommended is universally applicable to IaaS cloud systems and allows for the implementation of extremely flexible access control settings. An access control mechanism based on the Virtual Machine Manager (VMM) was also created, which allows the VM’s actions to be confined to the underlying resources at a finer level of detail. It has been shown in these researches that the implementation of the iHAC framework aids in the selection of real-world access control choices while imposing an acceptable performance cost on the system under examination.

In another research, it was shown that dynamic access control may be utilized to handle the many security threats that can arise in a cloud setting. Through the use of this technique, it is feasible to safeguard cloud data by taking into consideration the interrelationship between the requestor, the data that are being sought, and the action that will be taken on those data. The demands of the user were taken into account as well while offering dynamic access control. A first attempt at putting the anticipated method into action was all that was achieved as a consequence of the ultimate outcome.

Network-level solutions such as SNORT, an intrusion detection system for cloud computing, were offered by the authors under the Network-Level Solutions section as a network-level solution to prevent DoS and DDoS assaults. Such an attack floods the server with unnecessary packets, rendering it unusable for genuine users.

In order to recognize and prevent DDoS assaults, the suggested system takes use of certain criteria that have been set in advance of implementation. The authors outlined a strategy along the same lines, and they showed a mechanism for recognizing and filtering diverse DDoS assaults in cloud-based systems. When constructing this strategy, it is vital to use both the GARCH model and an artificial neural network to get the best results (ANN). When the actual value of variances is compared to a specified value of variances, Garch is used to calculate the value of variances and find any probable anomalies in real-world traffic. After values that are less than a certain threshold are eliminated, the ANN is used to categorize traffic into two categories: normal traffic and anomalous traffic. Normal traffic is defined as traffic that is less than a certain threshold.

Following the publication of a new article, users may randomly encrypt and push data blocks in a peer-to-peer network based on Blockchain by using a technique detailed in the study. In certain circumstances, the existence of several data centers and users in a distributed cloud might complicate the placement of file block copies, which can lead to performance issues. As a result, it seems that the Blockchain technique is the most favorable in terms of file security and network transmission time, respectively.