Buch, Englisch, 560 Seiten
Deploying Artificial Intelligence and Machine Learning
Buch, Englisch, 560 Seiten
ISBN: 978-1-394-30522-3
Verlag: Wiley
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.
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 multi-disciplinary 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.
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.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
Preface xxi
Part I: Foundations of AI & ML in Security 1
1 Foundations of AI and ML in Security 3
Sunil Kumar Mohapatra, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa Pattanaik
Abbreviations 4
1.1 Introduction 4
1.1.1 The Convergence of AI and ML in Security 5
1.2 Understanding Security Attacks 8
1.2.1 Types of Attacks and Vulnerability 9
1.2.2 How Attacks Exploit Vulnerabilities 10
1.2.3 Real-World Examples of AI and ML for Security 10
1.3 Evolution of Information, Cyber Issues/Threats Attacks 11
1.3.1 Cyber Security Threats 13
1.3.2 The Most Prevalent Security Attacks 14
1.4 Machine Learning for Security and Vulnerability 15
1.4.1 Data Collection and Preprocessing 16
1.4.2 Feature Engineering for Security Attack Detection 18
1.5 Challenges and Future Directions 20
1.6 Summary 22
References 23
2 Application of AI and ML in Threat Detection 29
Oviya Marimuthu, Priyadharshini Ravi and Senthil Janarthanan
2.1 Introduction 30
2.2 Foundation of AI and ML in Security 32
2.2.1 Definition and Concepts 32
2.2.2 Types of Artificial Intelligence 32
2.2.3 Algorithms and Models in Machine Learning 33
2.3 AI and ML in Applications in Threat Detection 34
2.3.1 Next-Generation Endpoint Protection 34
2.3.2 Endpoint Detection and Response (EDR) 35
2.4 AI/ML Based Network Intrusion Detection Systems (NIDS) 35
2.5 Threat Intelligence and Predictive Analytics 35
2.6 Challenges and Considerations 36
2.7 Integration and Interoperability 36
2.8 Future Directions 37
2.9 Conclusion 37
References 38
3 Artificial Intelligence and Machine Learning Applications in Threat Detection 41
Indu P.V., Preethi Nanjundan and Lijo Thomas
3.1 Introduction 42
3.2 Foundations of Threat Detection 42
3.2.1 Traditional Threat Detection Methods 43
3.2.2 The Need for Advanced Technologies 44
3.3 Overview of AI and ml 44
3.3.1 Understanding Artificial Intelligence 45
3.3.2 Machine Learning Fundamentals 45
3.4 AI and ML Techniques for Threat Detection 46
3.4.1 Supervised Learning and Unsupervised Learning 47
3.4.2 Deep Learning 47
3.5 Challenges and Solutions 48
3.5.1 Imbalanced Datasets 49
3.5.2 Ability and Interpretability 50
3.6 Future Trends and Innovations 51
3.6.1 Evolving Technologies 52
3.6.2 Ethical Considerations 52
Conclusion 53
References 54
Part II: AI & ML Applications in Threat Detection 57
4 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) 59
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 60
4.2 Related Work 62
4.3 Methodology 65
4.3.1 Data Preprocessing 65
4.3.2 Data Splitting 66
4.3.3 Machine Learning Models 66
4.4 Proposed Model 67
4.5 Experimental Result 68
4.5.1 Performance Evaluation Metrics 68
4.5.2 Results of XGBoost Classifier 69
4.5.2.1 Confusion Matrix 69
4.5.2.2 Accuracy/Recall/Precision 69
4.5.2.3 ROC Curve 71
4.5.3 Results of ExtraTrees Classifier 71
4.5.3.1 Accuracy/Recall/Precision/ROC Curve 71
4.5.4 Comparison and Discussion 73
4.6 Conclusion and Future Work 74
References 76
5 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection 79
Chandrakant Mallick, Parimal Kumar Giri, Mamata Garanayak and Sasmita Kumari Nayak
5.1 Introduction 80
5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions 80
5.1.2 Taxonomy of Cyber Attacks 81
5.2 Background 81
5.2.1 What is Cyber Security? 81
5.2.2 Cyber Security Systems 83
5.2.3 Ten Different Cyber Security Domains 85
5.3 Role of AI for Cyber Attack Detection 88
5.3.1 Machine Learning for Cyber Attack Detection 88
5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection 88
5.4 Cyber Security Data Sources and Feature Engineering 89
5.4.1 Data Sources 89
5.4.2 Feature Engineering 90
5.5 Training Models for Anomaly Detection in Network Traffic 91
5.5.1 Supervised Learning Models 91
5.5.2 Unsupervised Learning Models 91
5.5.3 Deep Learning Models 91
5.5.4 Hybrid Models 92
5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks 92
5.6.1 Analysis: Company X’s Strategy for Detecting Cyber Attacks 92
5.6.1.1 Implementation 92
5.6.1.2 Results 93
5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection 94
5.8 Future Trends 95
5.9 Conclusion 96
References 96
6 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform 101
Vijayadharshni, Krishan Shankash, Siddharth Tiwari, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
6.1 Introduction 102
6.2 Related Work 104
6.3 Methodology 105
6.3.1 Brief Code Explanation 105
6.3.1.1 Bringing in Libraries and Modules 105
6.3.1.2 Parting the Dataset 105
6.3.1.3 Making and Preparing the Model 105
6.3.1.4 Assessing the Model 106
6.3.1.5 Saving the Prepared Model 106
6.3.1.6 Stacking the Prepared Model 106
6.3.1.7 Information Assortment and Preprocessing 106
6.3.1.8 Extricating Remarkable IP Locations 107
6.3.1.9 Creating Highlights for IP Locations 107
6.3.1.10 Stacking Highlights Information 107
6.3.1.11 Foreseeing Needs 107
6.3.1.12 Printing IP Locations and Needs 107
6.3.2 Explanation of the Code Step-By-Step 108
6.4 Proposed Model 111
6.4.1 Workflow Model 111
6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study 112
6.5 Experimental Result/Result Analysis 113
6.6 Conclusion 115
6.6.1 High Level AI Calculations 115
6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies 116
6.6.3 Interpretability and Reasonableness 116
6.6.4 Taking Care of Information Changeability 116
6.6.5 Ill-Disposed Assault Recognition 116
6.6.6 Moral Contemplations 116
References 117
7 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches 119
Keshetti Sreekala, Maganti Venkatesh, M. V. Ramana Murthy, S. Venkata Meena, Srinivas Rathula and A. Lakshmanarao
7.1 Introduction 120
7.2 Literature Review 122
7.3 Proposed Methodology 124
7.4 Experimentation and Results 125
7.4.1 Data Assortment 125
7.4.2 Applying ML Algorithms 125
7.4.3 Apply FFNN 127
7.4.4 Apply Stacking Ensemble of RF and FFNN 127
7.4.5 Apply Voting Ensemble of RF and FFNN 127
7.4.6 Comparison of All Models 128
7.5 Conclusion 129
References 130
8 Intrusion Detection in Wireless Networks Using Novel Classification Models 131
Archith Gandla, Dinesh K., Vasu Gambhirrao, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi
8.1 Introduction 132
8.2 Literature Review 133
8.3 Methodology 138
8.4 State of the Art 140
8.5 Result Analysis 142
8.6 Conclusion 144
References 144
9 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model 147
G. Nithish Rao, J.M.S. Abhinav and M. Venkata Krishna Reddy
9.1 Introduction 148
9.2 Related Literature Survey 148
9.3 Proposed Framework 152
9.3.1 Block Diagram 153
9.3.2 Flow Chart 154
9.4 Implementation 154
9.4.1 Random Forest 155
9.4.2 XGBoost 155
9.4.3 CATBoost 155
9.5 Result Analysis 156
9.6 Conclusion 158
References 158
Part III: Advanced Security Solutions & Case Studies 161
10 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security 163
Adusumalli Balaji, T. Chaitanya, Tirupathi Rao Bammidi, Kanugo Sireesha and Dulam Devee Siva Prasad
10.1 Introduction 164
10.1.1 Background and Evolution of Cybersecurity Threats 164
10.1.2 The Need for Advanced Threat Detection 166
10.1.3 Review of Blockchain Technology in Cybersecurity 167
10.2 Understanding Blockchain Technology 169
10.2.1 Basics of Blockchain 170
10.2.2 Decentralization and Security Features 171
10.2.3 Smart Contracts and their Role in Security 172
10.3 Challenges in Traditional Threat Detection 173
10.3.1 Evolving Nature of Cyber Threats 174
10.3.2 The Importance of Proactive Security Solutions 177
10.4 Integrating Blockchain into Cybersecurity 178
10.4.1 Using Blockchain as the Basis for Improved Security 179
10.4.2 Consensus Mechanisms and Trust 181
10.4.3 Decentralized Identity Management 182
10.5 Challenges and Considerations of Blockchain in Cybersecurity 183
10.5.1 Scalability Issues in Blockchain 183
10.5.2 Regulatory and Compliance Challenges 183
10.5.3 Balancing Transparency and Privacy 184
10.6 Future Trends and Innovations and Case Studies of Blockchain Technology 184
10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security 184
10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions 186
10.7 Conclusion 188
References 188
11 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas 191
Chanumolu Kiran Kumar and Nandhakumar Ramachandran
11.1 Introduction 192
11.2 Literature Survey 195
11.3 Proposed Model 198
11.4 Results 205
11.5 Conclusion 214
References 214
12 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection 219
V. Sahiti Yellanki, B. Venkatesh, N. Sandhya and Neelima Gogineni
12.1 Introduction 220
12.2 Related Work 221
12.3 Existing System 222
12.4 Proposed System 225
12.5 Implementation 226
12.6 Results and Discussion 228
12.7 Conclusion 229
12.8 Future Scope 230
References 230
13 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques 233
Aravapalli Rama Satish and Sai Babu Veesam
13.1 Introduction 234
13.1.1 Overview of Video Summarization 234
13.1.2 Importance of Efficient Video Management 235
13.2 Video Summarization Techniques 237
13.2.1 Clustering-Based Methods 240
13.2.2 Deep Learning Frameworks 242
13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) 248
13.3 Notable Advanced Techniques 249
13.3.1 SVS_MCO Method and Performance 249
13.3.2 Knowledge Distillation (KDAN Framework) 250
13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) 251
13.4 Graph-Based and Unsupervised Summarization 252
13.4.1 Graph-Based Summarization Techniques 252
13.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) 252
13.5 Secure and Multi-Video Summarization 253
13.5.1 Secure Video Summarization 254
13.5.2 Multi-Video Summarization 254
13.6 Advanced Scene and Activity-Based Summarization 256
13.6.1 Scene Summarization 256
13.6.2 Activity Recognition 257
13.7 Performance Benchmarking and Evaluation 258
13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum) 258
13.7.2 Comparative Performance Analysis 260
13.8 Challenges and Future Directions 261
13.8.1 Current Limitations 261
13.8.2 Future Trends 262
13.9 Conclusion 263
References 264
14 Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study 267
Krishna Prasanna Gottumukkala, Sirikonda Manasa, Komal Chakravarthy and Kolikipogu Ramakrishna
14.1 Introduction 268
14.2 Literature Review 269
14.3 Methodology 272
14.4 Result Analysis 276
14.5 Conclusion 278
14.6 Acknowledgement 278
References 279
15 Detecting Low-Rate DDoS Attacks for CS 283
P. Venkata Kishore, B. Sivaneasan, Amjan Shaik and Prasun Chakrabarti
15.1 Introduction 284
15.2 Requirement Specification 284
15.3 Method and Technologies Involved 285
15.4 Testing and Validation 292
15.5 Results 293
15.6 Conclusion and Future Scope 297
References 297
16 Image Privacy Using Reversible Data Hiding and Encryption 301
Kiranmaie Puvulla, M. Venu Gopalachari, Sreeja Edla, Siddeshwar Vasam and Tushar Thakur
16.1 Introduction 302
16.2 Literature Survey 303
16.3 Methodology 305
16.4 Result Analysis 309
16.5 Conclusion 311
Acknowledgment 312
References 312
17 Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM) 315
Aarthi Pulivarthi, Jitta Poojitha Reddy, Vanka Eshwar Prabhas, T. Satyanarayana Murthy, Ramesh Babu and Ramu Kuchipudi
17.1 Introduction 316
17.2 Literature Review 318
17.3 Methodology 320
17.4 State of the Art 322
17.5 Results Analysis 323
17.5.1 Importing Libraries 323
17.5.2 Datasets 323
17.5.3 Model Comparison 324
17.6 Conclusion 325
Acknowledgment 326
References 326
18 Encryption and Decryption of Credit Card Data Using Quantum Cryptography 331
Sumit Ranjan, Armaan Munshi, Devansh Gupta, Sandeep Kumar Satapathy, Shruti Mishra, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu
18.1 Introduction 332
18.1.1 Evolution of Cryptography: A Historical Perspective 332
18.1.2 Quantum Cryptography: Unveiling the Quantum Revolution 333
18.1.3 Quantum Key Distribution Protocols and Practical Implementation 333
18.1.4 Encryption with Quantum Cryptography 333
18.1.5 Decryption with Quantum Cryptography 334
18.1.6 Challenges and Future Prospects 335
18.2 Related Works 335
18.3 Methodology 336
18.3.1 Quantum Key Distribution (QKD) Setup 336
18.3.2 Key Generation and Distribution 337
18.3.3 Encryption 337
18.3.4 Transmission 337
18.3.5 Decryption 337
18.3.6 Aes 338
18.4 Proposed Model 339
18.4.1 Key Generation 339
18.4.2 Encryption 340
18.4.3 Decryption 341
18.5 Experimental Result/Result Analysis 341
18.5.1 Flow Diagram of Quantum Cryptography Encryption and Decryption 341
18.5.2 Algorithm of the Code 343
18.6 Conclusion and Future Work 345
References 346
19 Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES 349
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 350
19.2 Related Work 353
19.3 Methodology 357
19.4 UML Diagram 359
19.5 Architecture Diagram 360
19.6 Implementation 360
19.7 Conclusion 361
References 362
20 Secure Pass: Hash-Based Password Generator and Checker with Randomized Function 365
Aneesh Rathore, Ganesh Choudhary, Mradul Goyal, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
20.1 Introduction 366
20.2 Related Work 368
20.3 Methodology 370
20.4 Conclusion and Future Work 376
References 377
21 Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security 379
Paras Yadav, Manya Bhardwaj, Akshita Bhamidimarri, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
21.1 Introduction 380
21.1.1 Problem Statement 380
21.1.2 Research Goals 381
21.2 Literature Review 382
21.3 Methodology 386
21.3.1 Face Recognition Algorithms and Techniques 387
21.3.2 Data Collection and Pre-Processing 387
21.3.3 Integration with Web Server Architecture 388
21.4 Proposed Model 389
21.5 Experimental Result/Result Analysis 394
21.5.1 Evaluation and Results 394
21.5.1.1 Performance Metrics for Face Authentication 394
21.5.1.2 Comparative Analysis Utilizing Password-Based Systems 395
21.5.1.3 Evaluation of Usability and User Experience 395
21.5.2 Security and Privacy Considerations 395
21.5.2.1 Implementing Measures to Safeguard Biometric Data 395
21.5.2.2 Vulnerability Analysis and Countermeasures 396
21.5.2.3 Legal and Ethical Considerations 396
21.6 Conclusion and Future Work 396
21.6.1 Contributions and Resulting Effects 397
21.6.2 Areas for Future Research Exploration 397
21.6.3 Implementation Recommendations 397
References 398
22 Cryptographic Key Application for Biometric Implementation in Automobiles 401
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 402
22.2 Related Work 405
22.3 Methodology 407
22.4 Proposed Methodology 409
22.5 Results and Analysis 414
22.6 Conclusion 415
References 417
23 Password Strength Testing: An Overview and Evaluation 419
Tanmay Agrawal, Kaushal Kanna, Azeem, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
23.1 Introduction 420
23.2 Related Work 421
23.3 Methodology 422
23.4 Result 425
23.5 Discussion 426
23.6 Conclusion 427
23.7 Future Work 428
References 429
24 Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks 431
Saswati Chatterjee, Suneeta Satpathy and Pratik Kumar Swain
24.1 Introduction 432
24.2 Background 433
24.2.1 Relevant Work 434
24.2.2 Cyber Kill Chain 434
24.2.3 SANS Artifacts Categorization 435
24.3 The D4I Framework 436
24.3.1 Mapping and Categorization of Digital Artifacts 436
24.3.2 A Way to Explain in Detail How to Examine and Analyze 437
24.4 Application Illustration 438
24.4.1 Integrating the D4I Framework with IoT Forensics 439
24.5 Discussion 440
24.6 Conclusion 441
References 442
25 Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy 445
R. S. M. Lakshmi Patibandla
25.1 Introduction 446
25.2 Security Challenges in AI-Based DR Diagnosis 450
25.2.1 Data Poisoning 450
25.2.2 Adversarial Attacks 451
25.2.3 Privacy Violations 452
25.3 Building Robust and Explainable AI Systems 453
25.3.1 Robust Model Design and Training 453
25.3.2 Data Augmentation to Enhance Model Generalizability 454
25.3.3 Interpretable Deep Learning and Explainable AI 456
25.3.4 Demystifying Deep Learning Predictions 458
25.3.5 Strict Data Governance and Privacy-Preserving Techniques 459
25.3.6 Performance of Strong Data Security Protocols 461
25.4 Benefits of Robust and Explainable AI 464
25.5 Conclusion: The Future of Secure AI in DR Diagnosis 468
References 468
26 Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare 473
Busa Shannu Sri, Kotha Dinesh Sai and U. M. Gopal Krishna
26.1 Introduction 474
26.2 Literature Review 474
26.3 Approaches 475
26.4 Analysis and Interpretation 477
26.5 Machine Learning and Security Considerations 480
26.6 Suggestions 480
26.7 Conclusion 481
References 482
27 Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection 483
M. Priyachitra, Prasanjit Singh, D. Senthil and Ellakkiya Sekar
27.1 Introduction 484
27.2 Allied Works 486
27.3 Proposed IDS Approach 488
27.3.1 Data Collection 489
27.3.2 Data Preprocessing 489
27.3.3 Feature Extraction 490
27.3.4 Intrusion Detection Using GRU 490
27.3.4.1 Gated Recurrent Unit 490
27.3.4.2 Optimization of GRU Using ACO Algorithm 492
27.4 Results and Discussion 493
27.4.1 Dataset Description 493
27.4.2 Performance Evaluation 493
27.4.3 Comparative Analysis 496
27.5 Conclusion 497
References 497
28 AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa 501
Shashi Kant and Metasebia Adula
28.1 Introduction 502
28.2 AI and ML Application in Cybersecurity Hazard Recognition 504
28.3 Detailed Applications of AI and ML in Ethiopia Perspectives 505
28.3.1 Variance Recognition in Ethiopia 505
28.3.1.1 Probable Challenges in Implementing AI and ML for Variance Recognition in Ethiopia 507
28.3.1.2 Opportunities in Implementing AI and ML Opportunities for Variance Recognition in Ethiopia 508
28.3.2 Intrusion Recognition and Princidenceion Softwares (IDPS) for Hazard Recognition in Ethiopia 510
28.3.2.1 Challenges That Arise When Learning AI and ML-Grounded IDPS Software’s in Ethiopia 511
28.3.2.2 Opportunities in Implementation of AI and ML-Grounded IDPS Software’s in Ethiopia 513
28.3.3 Browser Hijacking Software Recognition in Ethiopia 514
28.3.3.1 Challenges in Browser Hijacking Software Recognition in Ethiopia 516
28.3.3.2 Solutions for Browser Hijacking Software Recognition Challenge in Ethiopia 517
28.4 Scam and Deception Recognition in Ethiopia 518
28.4.1 Challenges in Scam and Deception Recognition in Ethiopia 519
28.4.2 Opportunities of AI and ML Application in Scam and Deception Recognition in Ethiopia 520
28.5 Hazard Acumen Examination in Ethiopia 522
28.5.1 Challenges in Hazard Acumen Examination in Ethiopia 523
28.5.2 AI and ML application in Hazard Acumen Examination in Ethiopia 524
28.6 AI and ML in Cybersecurity: Future Perspectives in Ethiopia 525
28.6.1 Future Perspectives 526
28.7 Conclusion 526
Acknowledgement 527
References 528
Index 531
