Protecting and Mitigating Against Cyber Threats

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. D. 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.

• Preface xxiPart I: Foundations of AI & ML in Security 11 Foundations of AI and ML in Security 3Sunil Kumar Mohapatra, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa PattanaikAbbreviations 41.1 Introduction 41.1.1 The Convergence of AI and ML in Security 51.2 Understanding Security Attacks 81.2.1 Types of Attacks and Vulnerability 91.2.2 How Attacks Exploit Vulnerabilities 101.2.3 Real-World Examples of AI and ML for Security 101.3 Evolution of Information, Cyber Issues/Threats Attacks 111.3.1 Cyber Security Threats 131.3.2 The Most Prevalent Security Attacks 141.4 Machine Learning for Security and Vulnerability 151.4.1 Data Collection and Preprocessing 161.4.2 Feature Engineering for Security Attack Detection 181.5 Challenges and Future Directions 201.6 Summary 22References 232 Application of AI and ML in Threat Detection 29Oviya Marimuthu, Priyadharshini Ravi and Senthil Janarthanan2.1 Introduction 302.2 Foundation of AI and ML in Security 322.2.1 Definition and Concepts 322.2.2 Types of Artificial Intelligence 322.2.3 Algorithms and Models in Machine Learning 332.3 AI and ML in Applications in Threat Detection 342.3.1 Next-Generation Endpoint Protection 342.3.2 Endpoint Detection and Response (EDR) 352.4 AI/ML Based Network Intrusion Detection Systems (NIDS) 352.5 Threat Intelligence and Predictive Analytics 352.6 Challenges and Considerations 362.7 Integration and Interoperability 362.8 Future Directions 372.9 Conclusion 37References 383 Artificial Intelligence and Machine Learning Applications in Threat Detection 41Indu P.V., Preethi Nanjundan and Lijo Thomas3.1 Introduction 423.2 Foundations of Threat Detection 423.2.1 Traditional Threat Detection Methods 433.2.2 The Need for Advanced Technologies 443.3 Overview of AI and ml 443.3.1 Understanding Artificial Intelligence 453.3.2 Machine Learning Fundamentals 453.4 AI and ML Techniques for Threat Detection 463.4.1 Supervised Learning and Unsupervised Learning 473.4.2 Deep Learning 473.5 Challenges and Solutions 483.5.1 Imbalanced Datasets 493.5.2 Ability and Interpretability 503.6 Future Trends and Innovations 513.6.1 Evolving Technologies 523.6.2 Ethical Considerations 52Conclusion 53References 54Part II: AI & ML Applications in Threat Detection 574 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) 59Aryan Kapoor, Jayasankar K.S., Pranay Jiljith, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty4.1 Introduction 604.2 Related Work 624.3 Methodology 654.3.1 Data Preprocessing 654.3.2 Data Splitting 664.3.3 Machine Learning Models 664.4 Proposed Model 674.5 Experimental Result 684.5.1 Performance Evaluation Metrics 684.5.2 Results of XGBoost Classifier 694.5.2.1 Confusion Matrix 694.5.2.2 Accuracy/Recall/Precision 694.5.2.3 ROC Curve 714.5.3 Results of ExtraTrees Classifier 714.5.3.1 Accuracy/Recall/Precision/ROC Curve 714.5.4 Comparison and Discussion 734.6 Conclusion and Future Work 74References 765 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection 79Chandrakant Mallick, Parimal Kumar Giri, Mamata Garanayak and Sasmita Kumari Nayak5.1 Introduction 805.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions 805.1.2 Taxonomy of Cyber Attacks 815.2 Background 815.2.1 What is Cyber Security? 815.2.2 Cyber Security Systems 835.2.3 Ten Different Cyber Security Domains 855.3 Role of AI for Cyber Attack Detection 885.3.1 Machine Learning for Cyber Attack Detection 885.3.2 Deep Learning as a Game Changer in Cyber Attack Detection 885.4 Cyber Security Data Sources and Feature Engineering 895.4.1 Data Sources 895.4.2 Feature Engineering 905.5 Training Models for Anomaly Detection in Network Traffic 915.5.1 Supervised Learning Models 915.5.2 Unsupervised Learning Models 915.5.3 Deep Learning Models 915.5.4 Hybrid Models 925.6 Case Study: The Use of AI and ML in Combating Cyber Attacks 925.6.1 Analysis: Company X’s Strategy for Detecting Cyber Attacks 925.6.1.1 Implementation 925.6.1.2 Results 935.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection 945.8 Future Trends 955.9 Conclusion 96References 966 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform 101Vijayadharshni, Krishan Shankash, Siddharth Tiwari, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty6.1 Introduction 1026.2 Related Work 1046.3 Methodology 1056.3.1 Brief Code Explanation 1056.3.1.1 Bringing in Libraries and Modules 1056.3.1.2 Parting the Dataset 1056.3.1.3 Making and Preparing the Model 1056.3.1.4 Assessing the Model 1066.3.1.5 Saving the Prepared Model 1066.3.1.6 Stacking the Prepared Model 1066.3.1.7 Information Assortment and Preprocessing 1066.3.1.8 Extricating Remarkable IP Locations 1076.3.1.9 Creating Highlights for IP Locations 1076.3.1.10 Stacking Highlights Information 1076.3.1.11 Foreseeing Needs 1076.3.1.12 Printing IP Locations and Needs 1076.3.2 Explanation of the Code Step-By-Step 1086.4 Proposed Model 1116.4.1 Workflow Model 1116.4.2 Decision Tree Machine Learning Model and Its Usage in this Study 1126.5 Experimental Result/Result Analysis 1136.6 Conclusion 1156.6.1 High Level AI Calculations 1156.6.2 Reconciliation of Regular Language Handling (NLP) Strategies 1166.6.3 Interpretability and Reasonableness 1166.6.4 Taking Care of Information Changeability 1166.6.5 Ill-Disposed Assault Recognition 1166.6.6 Moral Contemplations 116References 1177 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches 119Keshetti Sreekala, Maganti Venkatesh, M. V. Ramana Murthy, S. Venkata Meena, Srinivas Rathula and A. Lakshmanarao7.1 Introduction 1207.2 Literature Review 1227.3 Proposed Methodology 1247.4 Experimentation and Results 1257.4.1 Data Assortment 1257.4.2 Applying ML Algorithms 1257.4.3 Apply FFNN 1277.4.4 Apply Stacking Ensemble of RF and FFNN 1277.4.5 Apply Voting Ensemble of RF and FFNN 1277.4.6 Comparison of All Models 1287.5 Conclusion 129References 1308 Intrusion Detection in Wireless Networks Using Novel Classification Models 131Archith Gandla, Dinesh K., Vasu Gambhirrao, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi8.1 Introduction 1328.2 Literature Review 1338.3 Methodology 1388.4 State of the Art 1408.5 Result Analysis 1428.6 Conclusion 144References 1449 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model 147G. Nithish Rao, J.M.S. Abhinav and M. Venkata Krishna Reddy9.1 Introduction 1489.2 Related Literature Survey 1489.3 Proposed Framework 1529.3.1 Block Diagram 1539.3.2 Flow Chart 1549.4 Implementation 1549.4.1 Random Forest 1559.4.2 XGBoost 1559.4.3 CATBoost 1559.5 Result Analysis 1569.6 Conclusion 158References 158Part III: Advanced Security Solutions & Case Studies 16110 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security 163Adusumalli Balaji, T. Chaitanya, Tirupathi Rao Bammidi, Kanugo Sireesha and Dulam Devee Siva Prasad10.1 Introduction 16410.1.1 Background and Evolution of Cybersecurity Threats 16410.1.2 The Need for Advanced Threat Detection 16610.1.3 Review of Blockchain Technology in Cybersecurity 16710.2 Understanding Blockchain Technology 16910.2.1 Basics of Blockchain 17010.2.2 Decentralization and Security Features 17110.2.3 Smart Contracts and their Role in Security 17210.3 Challenges in Traditional Threat Detection 17310.3.1 Evolving Nature of Cyber Threats 17410.3.2 The Importance of Proactive Security Solutions 17710.4 Integrating Blockchain into Cybersecurity 17810.4.1 Using Blockchain as the Basis for Improved Security 17910.4.2 Consensus Mechanisms and Trust 18110.4.3 Decentralized Identity Management 18210.5 Challenges and Considerations of Blockchain in Cybersecurity 18310.5.1 Scalability Issues in Blockchain 18310.5.2 Regulatory and Compliance Challenges 18310.5.3 Balancing Transparency and Privacy 18410.6 Future Trends and Innovations and Case Studies of Blockchain Technology 18410.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security 18410.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions 18610.7 Conclusion 188References 18811 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas 191Chanumolu Kiran Kumar and Nandhakumar Ramachandran11.1 Introduction 19211.2 Literature Survey 19511.3 Proposed Model 19811.4 Results 20511.5 Conclusion 214References 21412 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection 219V. Sahiti Yellanki, B. Venkatesh, N. Sandhya and Neelima Gogineni12.1 Introduction 22012.2 Related Work 22112.3 Existing System 22212.4 Proposed System 22512.5 Implementation 22612.6 Results and Discussion 22812.7 Conclusion 22912.8 Future Scope 230References 23013 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques 233Aravapalli Rama Satish and Sai Babu Veesam13.1 Introduction 23413.1.1 Overview of Video Summarization 23413.1.2 Importance of Efficient Video Management 23513.2 Video Summarization Techniques 23713.2.1 Clustering-Based Methods 24013.2.2 Deep Learning Frameworks 24213.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) 24813.3 Notable Advanced Techniques 24913.3.1 SVS_MCO Method and Performance 24913.3.2 Knowledge Distillation (KDAN Framework) 25013.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) 25113.4 Graph-Based and Unsupervised Summarization 25213.4.1 Graph-Based Summarization Techniques 25213.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) 25213.5 Secure and Multi-Video Summarization 25313.5.1 Secure Video Summarization 25413.5.2 Multi-Video Summarization 25413.6 Advanced Scene and Activity-Based Summarization 25613.6.1 Scene Summarization 25613.6.2 Activity Recognition 25713.7 Performance Benchmarking and Evaluation 25813.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum) 25813.7.2 Comparative Performance Analysis 26013.8 Challenges and Future Directions 26113.8.1 Current Limitations 26113.8.2 Future Trends 26213.9 Conclusion 263References 26414 Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study 267Krishna Prasanna Gottumukkala, Sirikonda Manasa, Komal Chakravarthy and Kolikipogu Ramakrishna14.1 Introduction 26814.2 Literature Review 26914.3 Methodology 27214.4 Result Analysis 27614.5 Conclusion 27814.6 Acknowledgement 278References 27915 Detecting Low-Rate DDoS Attacks for CS 283P. Venkata Kishore, B. Sivaneasan, Amjan Shaik and Prasun Chakrabarti15.1 Introduction 28415.2 Requirement Specification 28415.3 Method and Technologies Involved 28515.4 Testing and Validation 29215.5 Results 29315.6 Conclusion and Future Scope 297References 29716 Image Privacy Using Reversible Data Hiding and Encryption 301Kiranmaie Puvulla, M. Venu Gopalachari, Sreeja Edla, Siddeshwar Vasam and Tushar Thakur16.1 Introduction 30216.2 Literature Survey 30316.3 Methodology 30516.4 Result Analysis 30916.5 Conclusion 311Acknowledgment 312References 31217 Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM) 315Aarthi Pulivarthi, Jitta Poojitha Reddy, Vanka Eshwar Prabhas, T. Satyanarayana Murthy, Ramesh Babu and Ramu Kuchipudi17.1 Introduction 31617.2 Literature Review 31817.3 Methodology 32017.4 State of the Art 32217.5 Results Analysis 32317.5.1 Importing Libraries 32317.5.2 Datasets 32317.5.3 Model Comparison 32417.6 Conclusion 325Acknowledgment 326References 32618 Encryption and Decryption of Credit Card Data Using Quantum Cryptography 331Sumit Ranjan, Armaan Munshi, Devansh Gupta, Sandeep Kumar Satapathy, Shruti Mishra, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu18.1 Introduction 33218.1.1 Evolution of Cryptography: A Historical Perspective 33218.1.2 Quantum Cryptography: Unveiling the Quantum Revolution 33318.1.3 Quantum Key Distribution Protocols and Practical Implementation 33318.1.4 Encryption with Quantum Cryptography 33318.1.5 Decryption with Quantum Cryptography 33418.1.6 Challenges and Future Prospects 33518.2 Related Works 33518.3 Methodology 33618.3.1 Quantum Key Distribution (QKD) Setup 33618.3.2 Key Generation and Distribution 33718.3.3 Encryption 33718.3.4 Transmission 33718.3.5 Decryption 33718.3.6 Aes 33818.4 Proposed Model 33918.4.1 Key Generation 33918.4.2 Encryption 34018.4.3 Decryption 34118.5 Experimental Result/Result Analysis 34118.5.1 Flow Diagram of Quantum Cryptography Encryption and Decryption 34118.5.2 Algorithm of the Code 34318.6 Conclusion and Future Work 345References 34619 Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES 349Yarradoddi Sai Sreenath Reddy, Gurram Thanmai, Kammila Charan Sri Sai Varma, Shruti Mishra, Sandeep Kumar Satapathy, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu19.1 Introduction 35019.2 Related Work 35319.3 Methodology 35719.4 UML Diagram 35919.5 Architecture Diagram 36019.6 Implementation 36019.7 Conclusion 361References 36220 Secure Pass: Hash-Based Password Generator and Checker with Randomized Function 365Aneesh Rathore, Ganesh Choudhary, Mradul Goyal, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty20.1 Introduction 36620.2 Related Work 36820.3 Methodology 37020.4 Conclusion and Future Work 376References 37721 Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security 379Paras Yadav, Manya Bhardwaj, Akshita Bhamidimarri, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty21.1 Introduction 38021.1.1 Problem Statement 38021.1.2 Research Goals 38121.2 Literature Review 38221.3 Methodology 38621.3.1 Face Recognition Algorithms and Techniques 38721.3.2 Data Collection and Pre-Processing 38721.3.3 Integration with Web Server Architecture 38821.4 Proposed Model 38921.5 Experimental Result/Result Analysis 39421.5.1 Evaluation and Results 39421.5.1.1 Performance Metrics for Face Authentication 39421.5.1.2 Comparative Analysis Utilizing Password-Based Systems 39521.5.1.3 Evaluation of Usability and User Experience 39521.5.2 Security and Privacy Considerations 39521.5.2.1 Implementing Measures to Safeguard Biometric Data 39521.5.2.2 Vulnerability Analysis and Countermeasures 39621.5.2.3 Legal and Ethical Considerations 39621.6 Conclusion and Future Work 39621.6.1 Contributions and Resulting Effects 39721.6.2 Areas for Future Research Exploration 39721.6.3 Implementation Recommendations 397References 39822 Cryptographic Key Application for Biometric Implementation in Automobiles 401Priyansh Chatap, Kavish Paul, Akshat Gupta, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty22.1 Introduction 40222.2 Related Work 40522.3 Methodology 40722.4 Proposed Methodology 40922.5 Results and Analysis 41422.6 Conclusion 415References 41723 Password Strength Testing: An Overview and Evaluation 419Tanmay Agrawal, Kaushal Kanna, Azeem, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty23.1 Introduction 42023.2 Related Work 42123.3 Methodology 42223.4 Result 42523.5 Discussion 42623.6 Conclusion 42723.7 Future Work 428References 42924 Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks 431Saswati Chatterjee, Suneeta Satpathy and Pratik Kumar Swain24.1 Introduction 43224.2 Background 43324.2.1 Relevant Work 43424.2.2 Cyber Kill Chain 43424.2.3 SANS Artifacts Categorization 43524.3 The D4I Framework 43624.3.1 Mapping and Categorization of Digital Artifacts 43624.3.2 A Way to Explain in Detail How to Examine and Analyze 43724.4 Application Illustration 43824.4.1 Integrating the D4I Framework with IoT Forensics 43924.5 Discussion 44024.6 Conclusion 441References 44225 Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy 445R. S. M. Lakshmi Patibandla25.1 Introduction 44625.2 Security Challenges in AI-Based DR Diagnosis 45025.2.1 Data Poisoning 45025.2.2 Adversarial Attacks 45125.2.3 Privacy Violations 45225.3 Building Robust and Explainable AI Systems 45325.3.1 Robust Model Design and Training 45325.3.2 Data Augmentation to Enhance Model Generalizability 45425.3.3 Interpretable Deep Learning and Explainable AI 45625.3.4 Demystifying Deep Learning Predictions 45825.3.5 Strict Data Governance and Privacy-Preserving Techniques 45925.3.6 Performance of Strong Data Security Protocols 46125.4 Benefits of Robust and Explainable AI 46425.5 Conclusion: The Future of Secure AI in DR Diagnosis 468References 46826 Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare 473Busa Shannu Sri, Kotha Dinesh Sai and U. M. Gopal Krishna26.1 Introduction 47426.2 Literature Review 47426.3 Approaches 47526.4 Analysis and Interpretation 47726.5 Machine Learning and Security Considerations 48026.6 Suggestions 48026.7 Conclusion 481References 48227 Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection 483M. Priyachitra, Prasanjit Singh, D. Senthil and Ellakkiya Sekar27.1 Introduction 48427.2 Allied Works 48627.3 Proposed IDS Approach 48827.3.1 Data Collection 48927.3.2 Data Preprocessing 48927.3.3 Feature Extraction 49027.3.4 Intrusion Detection Using GRU 49027.3.4.1 Gated Recurrent Unit 49027.3.4.2 Optimization of GRU Using ACO Algorithm 49227.4 Results and Discussion 49327.4.1 Dataset Description 49327.4.2 Performance Evaluation 49327.4.3 Comparative Analysis 49627.5 Conclusion 497References 49728 AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa 501Shashi Kant and Metasebia Adula28.1 Introduction 50228.2 AI and ML Application in Cybersecurity Hazard Recognition 50428.3 Detailed Applications of AI and ML in Ethiopia Perspectives 50528.3.1 Variance Recognition in Ethiopia 50528.3.1.1 Probable Challenges in Implementing AI and ML for Variance Recognition in Ethiopia 50728.3.1.2 Opportunities in Implementing AI and ML Opportunities for Variance Recognition in Ethiopia 50828.3.2 Intrusion Recognition and Princidenceion Softwares (IDPS) for Hazard Recognition in Ethiopia 51028.3.2.1 Challenges That Arise When Learning AI and ML-Grounded IDPS Software’s in Ethiopia 51128.3.2.2 Opportunities in Implementation of AI and ML-Grounded IDPS Software’s in Ethiopia 51328.3.3 Browser Hijacking Software Recognition in Ethiopia 51428.3.3.1 Challenges in Browser Hijacking Software Recognition in Ethiopia 51628.3.3.2 Solutions for Browser Hijacking Software Recognition Challenge in Ethiopia 51728.4 Scam and Deception Recognition in Ethiopia 51828.4.1 Challenges in Scam and Deception Recognition in Ethiopia 51928.4.2 Opportunities of AI and ML Application in Scam and Deception Recognition in Ethiopia 52028.5 Hazard Acumen Examination in Ethiopia 52228.5.1 Challenges in Hazard Acumen Examination in Ethiopia 52328.5.2 AI and ML application in Hazard Acumen Examination in Ethiopia 52428.6 AI and ML in Cybersecurity: Future Perspectives in Ethiopia 52528.6.1 Future Perspectives 52628.7 Conclusion 526Acknowledgement 527References 528Index 531