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Protecting and Mitigating Against Cyber Threats

Deploying Artificial Intelligence and Machine Learning

Edited by Sachi Nandan Mohanty, Suneeta Satpathy, Ming Yang and D. Khasim Vali
Copyright: 2025   |   Expected Pub Date:2025/03/30
ISBN: 9781394305223  |  Hardcover  |  
558 pages

One Line Description
The book provides invaluable insights into the transformative role of AI and ML
in security, offering essential strategies and real-world applications to effectively navigate the complex landscape of today’s cyber threats.

Audience
Cybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity.

Description
Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples.
Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multidisciplinary discussion influencing cybersecurity and digital resilience in the future.
Readers will find in this book:
• Provides comprehensive coverage on various aspects of security solicitations, ranging from theoretical foundations to practical applications;
• Includes real-world case studies and examples to illustrate how AI and machine learning technologies are currently utilized in security solicitations;
• Explores and discusses emerging trends at the intersection of AI, machine learning, and security solicitations, including topics like threat detection, fraud prevention, risk analysis, and more;
• Highlights the growing importance of AI and machine learning in security contexts and discusses the demand for knowledge in this area.

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Author / Editor Details
Sachi Nandan Mohanty, PhD is an associate professor at the School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India, He has published 60 articles in journals of international repute, edited 24 books, and serves as an editor for several international journals. His research interests include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, cognition, and computational intelligence.

Suneeta Satpathy, PhD is an associate professor in the Center for Artificial Intelligence and Machine Learning at Siksha O. Anusandhan University, India. She has published several papers in international journals and conferences of repute and edited numerous books. Her research interests include computer forensics, cyber security, data fusion, data mining, big data analysis, and decision mining.

Ming Yang, PhD is a professor in the College of Computing and Software Engineering at Kennesaw State University, Georgia, USA and serves as a consultant for many companies. He has published over 70 peer-reviewed conference and journal papers and book chapters in addition to serving as an editor for several journals. His research interests include image processing, multimedia communication, computer vision, and machine learning.

Khasim Vali, PhD is an assistant professor in the School of Computer Science and Engineering, the Vellore Institute of Technology, Andhra Pradesh University, India, with over 18 years of teaching experience. He has 21 international publications to his credit and is a life member of ISTE and IETE. His research interests include artificial intelligence, machine learning, and deep learning.

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Table of Contents
Preface
Part I: Foundations of AI & ML in Security
1. Foundations of AI and ML in Security
Sunil Kumar Mohapatra, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa Pattanaik
Abbreviations
1.1 Introduction
1.1.1 The Convergence of AI and ML in Security
1.2 Understanding Security Attacks
1.2.1 Types of Attacks and Vulnerability
1.2.2 How Attacks Exploit Vulnerabilities
1.2.3 Real-World Examples of AI and ML for Security
1.3 Evolution of Information, Cyber Issues/Threats Attacks
1.3.1 Cyber Security Threats
1.3.2 The Most Prevalent Security Attacks
1.4 Machine Learning for Security and Vulnerability
1.4.1 Data Collection and Preprocessing
1.4.2 Feature Engineering for Security Attack Detection
1.5 Challenges and Future Directions
1.6 Summary
References
2. Application of AI and ML in Threat Detection
Oviya Marimuthu, Priyadharshini Ravi and Senthil Janarthanan
2.1 Introduction
2.2 Foundation of AI and ML in Security
2.2.1 Definition and Concepts
2.2.2 Types of Artificial Intelligence
2.2.3 Algorithms and Models in Machine Learning
2.3 AI and ML in Applications in Threat Detection
2.3.1 Next-Generation Endpoint Protection
2.3.2 Endpoint Detection and Response (EDR)
2.4 AI/ML Based Network Intrusion Detection Systems (NIDS)
2.5 Threat Intelligence and Predictive Analytics
2.6 Challenges and Considerations
2.7 Integration and Interoperability
2.8 Future Directions
2.9 Conclusion
References
3. Artificial Intelligence and Machine Learning Applications in Threat Detection
Indu P.V., Preethi Nanjundan and Lijo Thomas
3.1 Introduction
3.2 Foundations of Threat Detection
3.2.1 Traditional Threat Detection Methods
3.2.2 The Need for Advanced Technologies
3.3 Overview of AI and ML
3.3.1 Understanding Artificial Intelligence
3.3.2 Machine Learning Fundamentals
3.4 AI and ML Techniques for Threat Detection
3.4.1 Supervised Learning and Unsupervised Learning
3.4.2 Deep Learning
3.5 Challenges and Solutions
3.5.1 Imbalanced Datasets
3.5.2 Ability and Interpretability
3.6 Future Trends and Innovations
3.6.1 Evolving Technologies
3.6.2 Ethical Considerations
Conclusion
References
Part II: AI & ML Applications in Threat Detection
4. Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS)
Aryan Kapoor, Jayasankar K.S., Pranay Jiljith, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
4.1 Introduction
4.2 Related Work
4.3 Methodology
4.3.1 Data Preprocessing
4.3.2 Data Splitting
4.3.3 Machine Learning Models
4.4 Proposed Model
4.5 Experimental Result
4.5.1 Performance Evaluation Metrics
4.5.2 Results of XGBoost Classifier
4.5.2.1 Confusion Matrix
4.5.2.2 Accuracy/Recall/Precision
4.5.2.3 ROC Curve
4.5.3 Results of ExtraTrees Classifier
4.5.3.1 Accuracy/Recall/Precision/ROC Curve
4.5.4 Comparison and Discussion
4.6 Conclusion and Future Work
References
5. Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection
Chandrakant Mallick, Parimal Kumar Giri, Mamata Garanayak and Sasmita Kumari Nayak
5.1 Introduction
5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions
5.1.2 Taxonomy of Cyber Attacks
5.2 Background
5.2.1 What is Cyber Security?
5.2.2 Cyber Security Systems
5.2.3 Ten Different Cyber Security Domains
5.3 Role of AI for Cyber Attack Detection
5.3.1 Machine Learning for Cyber Attack Detection
5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection
5.4 Cyber Security Data Sources and Feature Engineering
5.4.1 Data Sources
5.4.2 Feature Engineering
5.5 Training Models for Anomaly Detection in Network Traffic
5.5.1 Supervised Learning Models
5.5.2 Unsupervised Learning Models
5.5.3 Deep Learning Models
5.5.4 Hybrid Models
5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks
5.6.1 Analysis: Company X’s Strategy for Detecting Cyber Attacks
5.6.1.1 Implementation
5.6.1.2 Results
5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection
5.8 Future Trends
5.9 Conclusion
References
6. AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform
Vijayadharshni, Krishan Shankash, Siddharth Tiwari, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
6.1 Introduction
6.2 Related Work
6.3 Methodology
6.3.1 Brief Code Explanation
6.3.1.1 Bringing in Libraries and Modules
6.3.1.2 Parting the Dataset
6.3.1.3 Making and Preparing the Model
6.3.1.4 Assessing the Model
6.3.1.5 Saving the Prepared Model
6.3.1.6 Stacking the Prepared Model
6.3.1.7 Information Assortment and Preprocessing
6.3.1.8 Extricating Remarkable IP Locations
6.3.1.9 Creating Highlights for IP Locations
6.3.1.10 Stacking Highlights Information
6.3.1.11 Foreseeing Needs
6.3.1.12 Printing IP Locations and Needs
6.3.2 Explanation of the Code Step-By-Step
6.4 Proposed Model
6.4.1 Workflow Model
6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study
6.5 Experimental Result/Result Analysis
6.6 Conclusion
6.6.1 High Level AI Calculations
6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies
6.6.3 Interpretability and Reasonableness
6.6.4 Taking Care of Information Changeability
6.6.5 Ill-Disposed Assault Recognition
6.6.6 Moral Contemplations
References
7. Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches
Keshetti Sreekala, Maganti Venkatesh, M. V. Ramana Murthy, S. Venkata Meena, Srinivas Rathula and A. Lakshmanarao
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Experimentation and Results
7.4.1 Data Assortment
7.4.2 Applying ML Algorithms
7.4.3 Apply FFNN
7.4.4 Apply Stacking Ensemble of RF and FFNN
7.4.5 Apply Voting Ensemble of RF and FFNN
7.4.6 Comparison of All Models
7.5 Conclusion
References
8. Intrusion Detection in Wireless Networks Using Novel Classification Models
Archith Gandla, Dinesh K., Vasu Gambhirrao, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 State of the Art
8.5 Result Analysis
8.6 Conclusion
References
9. Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model
G. Nithish Rao, J.M.S. Abhinav and M. Venkata Krishna Reddy
9.1 Introduction
9.2 Related Literature Survey
9.3 Proposed Framework
9.3.1 Block Diagram
9.3.2 Flow Chart
9.4 Implementation
9.4.1 Random Forest
9.4.2 XGBoost
9.4.3 CATBoost
9.5 Result Analysis
9.6 Conclusion
References
Part III: Advanced Security Solutions & Case Studies
10. Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security
Adusumalli Balaji, T. Chaitanya, Tirupathi Rao Bammidi, Kanugo Sireesha and Dulam Devee Siva Prasad
10.1 Introduction
10.1.1 Background and Evolution of Cybersecurity Threats
10.1.2 The Need for Advanced Threat Detection
10.1.3 Review of Blockchain Technology in Cybersecurity
10.2 Understanding Blockchain Technology
10.2.1 Basics of Blockchain
10.2.2 Decentralization and Security Features
10.2.3 Smart Contracts and their Role in Security
10.3 Challenges in Traditional Threat Detection
10.3.1 Evolving Nature of Cyber Threats
10.3.2 The Importance of Proactive Security Solutions
10.4 Integrating Blockchain into Cybersecurity
10.4.1 Using Blockchain as the Basis for Improved Security
10.4.2 Consensus Mechanisms and Trust
10.4.3 Decentralized Identity Management
10.5 Challenges and Considerations of Blockchain in Cybersecurity
10.5.1 Scalability Issues in Blockchain
10.5.2 Regulatory and Compliance Challenges
10.5.3 Balancing Transparency and Privacy
10.6 Future Trends and Innovations and Case Studies of Blockchain Technology
10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security
10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions
10.7 Conclusion
References
11. Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas
Chanumolu Kiran Kumar and Nandhakumar Ramachandran
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Model
11.4 Results
11.5 Conclusion
References
12. Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection
V. Sahiti Yellanki, B. Venkatesh, N. Sandhya and Neelima Gogineni
12.1 Introduction
12.2 Related Work
12.3 Existing System
12.4 Proposed System
12.5 Implementation
12.6 Results and Discussion
12.7 Conclusion
12.8 Future Scope
References
13. Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques
Aravapalli Rama Satish and Sai Babu Veesam
13.1 Introduction
13.1.1 Overview of Video Summarization
13.1.2 Importance of Efficient Video Management
13.2 Video Summarization Techniques
13.2.1 Clustering-Based Methods
13.2.2 Deep Learning Frameworks
13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual)
13.3 Notable Advanced Techniques
13.3.1 SVS_MCO Method and Performance
13.3.2 Knowledge Distillation (KDAN Framework)
13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks)
13.4 Graph-Based and Unsupervised Summarization
13.4.1 Graph-Based Summarization Techniques
13.4.2 Unsupervised Summarization Methods (Two-Stream Approach for Motion and Visual Features)
13.5 Secure and Multi-Video Summarization
13.5.1 Secure Video Summarization
13.5.2 Multi-Video Summarization
13.6 Advanced Scene and Activity-Based Summarization
13.6.1 Scene Summarization
13.6.2 Activity Recognition
13.7 Performance Benchmarking and Evaluation
13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum)
13.7.2 Comparative Performance Analysis
13.8 Challenges and Future Directions
13.8.1 Current Limitations
13.8.2 Future Trends
13.9 Conclusion
References
14. Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study
Krishna Prasanna Gottumukkala, Sirikonda Manasa, Komal Chakravarthy and Kolikipogu Ramakrishna
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.4 Result Analysis
14.5 Conclusion
14.6 Acknowledgement
References
15. Detecting Low-Rate DDoS Attacks for CS
P. Venkata Kishore, B. Sivaneasan, Amjan Shaik and Prasun Chakrabarti
15.1 Introduction
15.2 Requirement Specification
15.3 Method and Technologies Involved
15.4 Testing and Validation
15.5 Results
15.6 Conclusion and Future Scope
References
16. Image Privacy Using Reversible Data Hiding and Encryption
Kiranmaie Puvulla, M. Venu Gopalachari, Sreeja Edla, Siddeshwar Vasam and Tushar Thakur
16.1 Introduction
16.2 Literature Survey
16.3 Methodology
16.4 Result Analysis
16.5 Conclusion
Acknowledgment
References
17. Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM)
Aarthi Pulivarthi, Jitta Poojitha Reddy, Vanka Eshwar Prabhas, T. Satyanarayana Murthy, Ramesh Babu and Ramu Kuchipudi
17.1 Introduction
17.2 Literature Review
17.3 Methodology
17.4 State of the Art
17.5 Results Analysis
17.5.1 Importing Libraries
17.5.2 Datasets
17.5.3 Model Comparison
17.6 Conclusion
Acknowledgment
References
18. Encryption and Decryption of Credit Card Data Using Quantum Cryptography
Sumit Ranjan, Armaan Munshi, Devansh Gupta, Sandeep Kumar Satapathy, Shruti Mishra, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu
18.1 Introduction
18.1.1 Evolution of Cryptography: A Historical Perspective
18.1.2 Quantum Cryptography: Unveiling the Quantum Revolution
18.1.3 Quantum Key Distribution Protocols and Practical Implementation
18.1.4 Encryption with Quantum Cryptography
18.1.5 Decryption with Quantum Cryptography
18.1.6 Challenges and Future Prospects
18.2 Related Works
18.3 Methodology
18.3.1 Quantum Key Distribution (QKD) Setup
18.3.2 Key Generation and Distribution
18.3.3 Encryption
18.3.4 Transmission
18.3.5 Decryption
18.3.6 AES
18.4 Proposed Model
18.4.1 Key Generation
18.4.2 Encryption
18.4.3 Decryption
18.5 Experimental Result/Result Analysis
18.5.1 Flow Diagram of Quantum Cryptography Encryption and Decryption
18.5.2 Algorithm of the Code
18.6 Conclusion and Future Work
References
19. Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES
Yarradoddi Sai Sreenath Reddy, Gurram Thanmai, Kammila Charan Sri Sai Varma, Shruti Mishra, Sandeep Kumar Satapathy, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu
19.1 Introduction
19.2 Related Work
19.3 Methodology
19.4 UML Diagram
19.5 Architecture Diagram
19.6 Implementation
19.7 Conclusion
References
20. Secure Pass: Hash-Based Password Generator and Checker with Randomized Function
Aneesh Rathore, Ganesh Choudhary, Mradul Goyal, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
20.1 Introduction
20.2 Related Work
20.3 Methodology
20.4 Conclusion and Future Work
References
21. Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security
Paras Yadav, Manya Bhardwaj, Akshita Bhamidimarri, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
21.1 Introduction
21.1.1 Problem Statement
21.1.2 Research Goals
21.2 Literature Review
21.3 Methodology
21.3.1 Face Recognition Algorithms and Techniques
21.3.2 Data Collection and Pre-Processing
21.3.3 Integration with Web Server Architecture
21.4 Proposed Model
21.5 Experimental Result/Result Analysis
21.5.1 Evaluation and Results
21.5.1.1 Performance Metrics for Face Authentication
21.5.1.2 Comparative Analysis Utilizing Password-Based Systems
21.5.1.3 Evaluation of Usability and User Experience
21.5.2 Security and Privacy Considerations
21.5.2.1 Implementing Measures to Safeguard Biometric Data
21.5.2.2 Vulnerability Analysis and Countermeasures
21.5.2.3 Legal and Ethical Considerations
21.6 Conclusion and Future Work
21.6.1 Contributions and Resulting Effects
21.6.2 Areas for Future Research Exploration
21.6.3 Implementation Recommendations
References
22. Cryptographic Key Application for Biometric Implementation in Automobiles
Priyansh Chatap, Kavish Paul, Akshat Gupta, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
22.1 Introduction
22.2 Related Work
22.3 Methodology
22.4 Proposed Methodology
22.5 Results and Analysis
22.6 Conclusion
References
23. Password Strength Testing: An Overview and Evaluation
Tanmay Agrawal, Kaushal Kanna, Azeem, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
23.1 Introduction
23.2 Related Work
23.3 Methodology
23.4 Result
23.5 Discussion
23.6 Conclusion
23.7 Future Work
References
24. Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks
Saswati Chatterjee, Suneeta Satpathy and Pratik Kumar Swain
24.1 Introduction
24.2 Background
24.2.1 Relevant Work
24.2.2 Cyber Kill Chain
24.2.3 SANS Artifacts Categorization
24.3 The D4I Framework
24.3.1 Mapping and Categorization of Digital Artifacts
24.3.2 A Way to Explain in Detail How to Examine and Analyze
24.4 Application Illustration
24.4.1 Integrating the D4I Framework with IoT Forensics
24.5 Discussion
24.6 Conclusion
References
25. Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy
R. S. M. Lakshmi Patibandla
25.1 Introduction
25.2 Security Challenges in AI-Based DR Diagnosis
25.2.1 Data Poisoning
25.2.2 Adversarial Attacks
25.2.3 Privacy Violations
25.3 Building Robust and Explainable AI Systems
25.3.1 Robust Model Design and Training
25.3.2 Data Augmentation to Enhance Model Generalizability
25.3.3 Interpretable Deep Learning and Explainable AI
25.3.4 Demystifying Deep Learning Predictions
25.3.5 Strict Data Governance and Privacy-Preserving Techniques
25.3.6 Performance of Strong Data Security Protocols
25.4 Benefits of Robust and Explainable AI
25.5 Conclusion: The Future of Secure AI in DR Diagnosis
References
26. Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare
Busa Shannu Sri, Kotha Dinesh Sai and U. M. Gopal Krishna
26.1 Introduction
26.2 Literature Review
26.3 Approaches
26.4 Analysis and Interpretation
26.5 Machine Learning and Security Considerations
26.6 Suggestions
26.7 Conclusion
References
27. Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection
M. Priyachitra, Prasanjit Singh, D. Senthil and Ellakkiya Sekar
27.1 Introduction
27.2 Allied Works
27.3 Proposed IDS Approach
27.3.1 Data Collection
27.3.2 Data Preprocessing
27.3.3 Feature Extraction
27.3.4 Intrusion Detection Using GRU
27.3.4.1 Gated Recurrent Unit
27.3.4.2 Optimization of GRU Using ACO Algorithm
27.4 Results and Discussion
27.4.1 Dataset Description
27.4.2 Performance Evaluation
27.4.3 Comparative Analysis
27.5 Conclusion
References
28. AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa
Shashi Kant and Metasebia Adula
28.1 Introduction
28.2 AI and ML Application in Cybersecurity Hazard Recognition
28.3 Detailed Applications of AI and ML in Ethiopia Perspectives
28.3.1 Variance Recognition in Ethiopia
28.3.1.1 Probable Challenges in Implementing AI and ML for Variance Recognition in Ethiopia
28.3.1.2 Opportunities in Implementing AI and ML Opportunities for Variance Recognition in Ethiopia
28.3.2 Intrusion Recognition and Princidenceion Softwares (IDPS) for Hazard Recognition in Ethiopia
28.3.2.1 Challenges That Arise When Learning AI and ML-Grounded IDPS Software’s in Ethiopia
28.3.2.2 Opportunities in Implementation of AI and ML-Grounded IDPS Software’s
in Ethiopia
28.3.3 Browser Hijacking Software Recognition in Ethiopia
28.3.3.1 Challenges in Browser Hijacking Software Recognition in Ethiopia
28.3.3.2 Solutions for Browser Hijacking Software Recognition Challenge in Ethiopia
28.4 Scam and Deception Recognition in Ethiopia
28.4.1 Challenges in Scam and Deception Recognition in Ethiopia
28.4.2 Opportunities of AI and ML Application in Scam and Deception Recognition in Ethiopia
28.5 Hazard Acumen Examination in Ethiopia
28.5.1 Challenges in Hazard Acumen Examination in Ethiopia
28.5.2 AI and ML application in Hazard Acumen Examination in Ethiopia
28.6 AI and ML in Cybersecurity: Future Perspectives in Ethiopia
28.6.1 Future Perspectives
28.7 Conclusion
Acknowledgement
References
Index

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