Next Generation Email Security: AI Based Spam Detection

Dr. Ramjee Prasad’s accolades include authored books, chapters, 200+ peer-reviewed publications, and leadership roles in EU and US research initiatives. His career is defined by a relentless focus on secure, sustainable, and socially responsible solutions, principles he continues to advance through his entrepreneurial ventures and global partnerships. Ramjee Prasad, Fellow IEEE, IET, IETE, and WWRF, is a former Professor Emeritus of Future Technologies for Business Ecosystem Innovation (FT4BI) in the Department of Business Development and Technology, Aarhus University, Herning, Denmark. He is the Founder President of the CTIF Global Capsule (CGC) and the Founder Chairman of the Global ICT Standardization Forum for India, established in 2009. He was honored by the University of Rome “Tor Vergata,” Italy as a Distinguished Professor of the Department of Clinical Sciences and Translational Medicine in 2016. He is an Honorary Professor at the University of Cape Town, South Africa and the University of KwaZulu-Natal, South Africa, and also an Adjunct Professor at Birsa Institute of Technology, Sindri, Jharkhand, India. He received Pravasi Bhartiya Samman Puraskaar (Emigrant Indian Honor Award by the Indian President) in 2023 and the Ridderkorset of Dannebrogordenen (Knight of the Dannebrog) in 2010 from the Danish Queen for the internationalization of top-class telecommunication research and education. He has received several international awards such as the IEEE Communications Society Wireless Communications Technical Committee Recognition Award in 2003 for making a contribution in the field of “Personal, Wireless and Mobile Systems and Networks,” Telenor’s Research Award in 2005 for impressive merits, both academic and organizational within the field of wireless and personal communication, and 2014 IEEE AESS Outstanding Organizational Leadership Award for “Organizational Leadership in developing and globalizing the CTIF (Center for TeleInFrastruktur) Research Network”. He has been the Project Coordinator of several EC projects, namely, MAGNET, MAGNET Beyond, and eWALL. He has published more than 50 books, 1000 plus journal and conference publications, more than 15 patents, over 150 Ph.D. Graduates and a larger number of masters (over 250). Several of his students are today’s worldwide telecommunication leaders themselvesDr. Vikas Kadam is an accomplished researcher and Associate Professor in the Information Technology Department at Marathwada Mitra Mandal’s College of Engineering, Pune. His research primarily focuses on cybersecurity, machine learning, and deep learning algorithms, with a special emphasis on spam detection using advanced machine learning techniques. He has contributed significantly to the field through several high-impact publications, including “Enhancement of Email Spam Detection Using Improved Deep Learning Algorithms for Cyber Security” published in the Journal of Computer Security (2022) and “A Hybrid Meta-Heuristic-Based Multi-Objective Feature Selection with Adaptive Capsule Network for Automated Email Spam Detection” in the International Journal of Intelligent Robotics and Applications (2022). His latest work, “Designing a Novel Framework of Email Spam Detection Using an Improved Heuristic Algorithm and Dual-Scale Feature Fusion-Based Adaptive Convolution Neural Network”, was published in Information Security Journal: A Global Perspective (2025). Dr. Kadam holds a Ph.D. in Spam Detection Using Machine Learning Algorithms and was recognized for his excellence with the EMC² Star Performer Award in 2015. His research continues to contribute toward enhancing cybersecurity frameworks and intelligent systems, helping address critical challenges in digital communication security.Vandana Rohokale received her B.Eng. degree in Electronics Engineering in 1997 from Pune University, Maharashtra, India. She received her master’s degree in Electronics in 2007 from Shivaji University, Kolhapur, Maharashtra, India. She received her Ph.D. degree in Wireless Communication in 2013 from CTIF, Aalborg University, Denmark. She completed her post doctorate from ARHUS University under the guidance of Prof. Ramjee Prasad in 2023. She is presently working as Professor and Vice Principal in Sinhgad Institute of Technology and Science, Pune, Maharashtra, India. Her teaching experience is around 28 years. She has published four books with international publishers. She has published around 60 plus papers in various international journals and conferences. Her research interests include cooperative wireless communications, adhoc and cognitive networks, physical layer security, digital signal processing, information theoretic security and its applications, cyber security, artificial intelligence and machine learning, quantum computing, etc

• 1. Introduction 1.1 Emergence of Online Social Networks 1.2 Challenges of Social Network Security 1.3 Email spam -An issue in Cyber Security 1.4 Origins of Email spam 1.5 Types of Spam 1.6 Spam avoiding Techniques 1.6.1 Technical Techniques 1.6.2 Non-Technical Techniques 1.7 Filtering Techniques For Email Spam Detection 1.8 Existing Email Spam Detection Models 1.9 Summary 1. Introduction 1.1 Emergence of Online Social Networks 1.2 Challenges of Social Network Security 1.3 Email spam -An issue in Cyber Security 1.4 Origins of Email spam 1.5 Types of Spam 1.6 Spam avoiding Techniques 1.6.1 Technical Techniques 1.6.2 Non-Technical Techniques 1.7 Filtering Techniques For Email Spam Detection 1.8 Existing Email Spam Detection Models 1.9 Summary 2. Email spam detection models Overview 2.1 Supervised Deep Learning based models 2.2 Supervised Machine Lerning based models 2.3 Enhanced Heuristic –based models 2.4 Unsupervised -Clustering based models 2.5 Hybrid spam detection models 2.6 Unaddressed challenges 3. Email spam detection approach - I Fitness Oriented Levy Improvement-Based Dragonfly Algorithm -An improved email spam detection deep learning approach 3.1 Introduction 3.2 Architecture of FLIDA email spam detection technique 3.3 Datasets Description 3.4 Extraction of Text Features using TFIDF 3.5 Extraction of Visual Features using GLCM and Color Correlogram 3.6 Selection of Optimal Features 3.7 Optimal Feature Selection and Classification Using Levy Improvement – Based Dragonfly Algorithm 3.8 Conventional Dragonfly Algorithm 3.9 Implementation of FLI-DA 3.10 Classification of image and text features using hybrid model 4. Performance Analysis of FLIDA 4.1 Experimental setup and parameter setting 4.2 Performance Analysis of FLIDA in Terms Of Accuracy with different optimization algorithms 4.3 Performance Analysis of FLIDA Using Different Machine Learning And Deep Learning Models 4.4 Performance analysis of proposed FLI-DA-CRNN Email spam detection model 4.5 Performance Analysis of FLIDA with ensemble approaches 4.5 Error and Feature Analysis of Email Spam Detection Techniques 4.6 Analysing Implementation Time 4.7 Conclusion 5. Email spam detection approach II Adaptive Capsule Network based, -An automated email spam detection approach 5.1 Introduction 5.2 Architecture of proposed email spam detection technique using G- SFO Algorithm 5.3 Extraction of Visual Features 5.3.1 Walsh-Hadamard Transform Matrix 5.3.2 Fisher Discriminate Analysis 5.3.3 Color Correlogram 5.4 Extraction of Text Features using 5.4.1 TV 5.4.2 TFIDF 5.5 Architecture of A-CapsNet framework for email spam detection 5.6 G-SFO - Grey-Sail Fish Optimization Algorithm 5.6.1 MFOS- Multi-objective Feature Selection 6. Performance Analysis of G-SFO with Acaps Network 6.1 Experimental setup and parameter setting 6.2 Dataset Used 6.3 Evaluation Metrics 6.4 Algorithmic Evaluation of the Suggested G-SFO with ACapsNet Model 6.5 Performance Analysis of the G-SFO ACapsNet Model with machine and deep learning models 6.6 Performance analysis of the G-SFO ACapsNet Model with Existing Metaheuristic Algorithms 6.7 Performance Validation of the Proposed Spam Email Detection Framework against Traditional Classifiers 6.8 Summary 7. Comparative Analysis of Approach I- FLIDA and Approach II- G-SFO ACapsNet 7.1 Introduction 7.2 Comparative Analysis of Approach I- FLIDA and Approach II- G-SFO ACapsNet on Dataset 1 7.3 Comparative Analysis of Approach I- FLIDA and Approach II- G-SFO ACapsNet on Dataset 2 7.4 Comparative Analysis of Approach I- FLIDA and Approach II- G-SFO ACapsNet on Dataset 3 7.5 Comparative Analysis of Approach I- FLIDA and Approach II- G-SFO ACapsNet on Dataset 4 7.6 Summary Chapter 8: Quantum Machine Learning for Email Spam Detection 8.1 Email Spam 8.2 Conventional Spam Detection Methods 8.2.1 Early Rule-Based Methods 8.2.2 Statistical Models and Machine Learning 8.3 Limitations of Conventional Methods 8.3.1 Scalability Problems 8.3.2 Dimensionality Issue 8.3.3 Computational Inadequacies 8.4 Quantum Machine Learning in Spam Detection 8.4.1 Quantum Speedup 8.4.2 Addressing Feature and Data Complexity 8.5 Conventional Machine Learning Techniques for Spam Detection 8.5.1 Supervised Learning Methods for Spam Detection 8.5.1.1 Support Vector Machines (SVMs) 8.5.1.2 Naive Bayes Classifier 8.5.1.3 Decision Trees and Random Forests 8.5.1.4 Deep Neural Networks (DNNs) 8.5.2 Feature Extraction in Spam Detection 8.5.2.1 Text Features 8.5.2.2 Metadata Features 8.5.2.3 User Behavioral Features 8.6 Fundamentals of Quantum Computing: Key Concepts 8.6.1 Qubits and Superposition 8.6.2 Quantum Entanglement 8.6.3 Quantum Interference 8.6.4 Quantum Gates and Circuits 8.6.4.1 Pauli Gates 8.6.4.2 Hadamard Gate 8.6.4.3 CNOT Gate and Controlled Operations 8.6.5 Quantum Measurement 8.7 Quantum Machine Learning Algorithms for Spam Detection 8.7.1 Quantum Support Vector Machines (QSVM) 8.7.1.1 Quantum Feature Maps 8.7.1.2 Speedup with Quantum Kernels 8.7.2 Quantum k-Nearest Neighbors (QkNN) 8.7.2.1 Quantum Distance Computation 8.7.2.2 Advantages over Classical K-NN 8.7.3 Quantum Neural Networks (QNN) 8.7.3.1 Training Quantum Neural Networks 8.7.3.2 High Dimensional Data Representation 8.7.4 Variational Quantum Algorithms (VQA) 8.7.4.1 Hybrid Quantum- Classical Approaches 8.7.4.2 Applications in Spam Detection 8.8 Challenges in Quantum Machine Learning for Spam Detection 8.8.1 Noisy Intermediate-Scale Quantum (NISQ) Devices 8.8.2 Quantum Data Encoding 8.8.3 Scalability of Quantum Algorithms 8.9 Future Directions in Quantum Machine Learning for Spam Detection 8.9.1 Advances in Quantum Hardware 8.9.2 Hybrid Quantum-Classical Models 8.9.3 Quantum- Enhanced Feature Extraction 8.10 Cybersecurity with Quantum Machine Learning 8.10.1 QML for Email Spam Detection 8.10.2 Email Spam Detection Research 8.11 Summary Chapter G. Conclusion and Future Scope 9.1 Conclusions 9.2 Major Findings 9.3 Future Scope Chapter 9. Conclusion and Future Scope 9.1 Conclusions References