Next Generation Email Security: AI Based Spam Detection

A technical guide to AI-driven email protection 🔐

Email remains one of the most attractive entry points for spam, phishing, and malware. Next Generation Email Security: AI Based Spam Detection addresses that problem as a research and engineering challenge: how can detection systems keep pace with evolving attacks while handling the scale and variety of real-world email? Published by River Publishers in 2026, the book brings together classical spam-filtering knowledge, machine-learning models, and newer quantum-computing ideas in one technical treatment.

From classical filters to modern detection models

Early chapters map the terrain. They discuss the emergence of online social networks, the security challenges that come with them, and the specific issue of email spam within cybersecurity. The book reviews filtering and spam-avoidance techniques, then surveys existing detection models: supervised deep learning, supervised machine learning, enhanced heuristics, unsupervised clustering, and hybrid designs. Rather than presenting a single method as a solution, it identifies where conventional approaches struggle and what remains unresolved.

Two proposed frameworks: FLI-DA and G-SFO with A-CapsNet 💡

The core of the book introduces two novel frameworks. FLI-DA combines text features extracted with TF-IDF, visual features derived from GLCM and color correlograms, optimal feature selection, and a dragonfly-inspired optimization approach, followed by classification with a hybrid model. The second framework, G-SFO with adaptive capsule networks (A-CapsNet), explores gray-sail fish optimization and capsule-network architecture for automated spam detection. Performance chapters evaluate accuracy, implementation time, ensembles, feature behavior, and comparative results.

Quantum machine learning and future directions

A later section turns to quantum machine learning for email spam detection. It outlines key quantum-computing concepts, explains limitations of conventional methods, surveys quantum machine-learning algorithms, and considers challenges, cybersecurity implications, and future directions. This part is especially useful for readers tracking where AI and quantum paradigms may intersect in secure communications.

Who will find it useful 🧠

The book is written for a research-oriented audience: cybersecurity and AI researchers, postgraduate students, data scientists, and professionals working on secure communication technologies. Industry practitioners and policymakers evaluating email-security architecture may also find the comparative analysis and open research issues useful. The treatment is technical and aimed at readers with a background or active interest in machine learning or cybersecurity.

What sets it apart ⚙️

• A concise review of classical and AI-based spam detection.
• Two distinctive proposed frameworks: FLI-DA and G-SFO–A-CapsNet.
• Empirical evaluations and comparative performance analyses.
• Coverage of quantum machine learning as a forward-looking security paradigm.
• Discussion of open research issues and future perspectives.

For readers who want a technically grounded view of where email spam detection has been and where AI may take it next, this River Publishers title offers a focused research reference.