Artificial Intelligence and Cybersecurity

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Artificial Intelligence and Cybersecurity: Advances and Innovations by Ishaani Priyadarshini

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This book will make the readers aware of the current state of the art of artificial intelligence in cybersecurity. It includes content related to various types of artificial intelligence techniques including machine learning and natural language processing and therefore highlights the impact of artificial intelligence in cybersecurity.

Considering the current state, future research directions may be obtained from the book.

Chapter 1 discusses research into the classification of heart disease models from 303 datasets taken from the UCI repository. Before classification of data, cleaning and filtering was performed to eliminate the null values and identify the potential attributes. Among the four classification models: kNN, DT, XGBoost and random forest, the best classification models were identified on the basis of accuracy: area under ROC (AUC) and training time.

In Chapter 2, photonic crystals (PhC) have been utilised due to their generous structures and flexibility to adapt to every field. A dual core PCF refractive index sensor is proposed with the aim of improving performance, sensing ability, accuracy and selectivity by using identified AI algorithms. Tumor cells have refractive indices between 1.3342 and 1.4251. The maximum sensitivity observed was 32,358 nm/RIU and the minimum sensitivity observed was 11,258 nm/RIU. The highest accuracy reported for SVM was 96%.

The aim of Chapter 3 is to provide a brief overview of security issues in the IoT and discuss possible countermeasures. This work highlights various security mechanisms adopted by IoT services and explores the most emerging IoT communication technology protocols. Further, this work provides the new scope to overcome threats and cyberattacks in an efficient and automated manner using computational intelligence technologies.

The goals of Chapter 4 are to deliver an in-depth review of cybersecurity-oriented IoT architecture, provide insights into cyberthreats for the IoT, provide security necessities, outline the cyber-challenges and give visions on how these challenges can be overcome. Finally, the chapter offers new directions and research trends into cybersecurity and the IoT.

The focus of Chapter 5 is to highlight the layered security architecture and working of the IoT. This work also throws light on the major threats and challenges faced by Industry 4.0 and other related technologies. Next, it outlines the proposed solutions to cyberthreats in Industry 4.0. Further, this work highlights the need for blockchain technology in Industry 4.0 and presents a detailed discussion on various application scenarios.

Chapter 6 discusses the political, social and economic issues plaguing farmers and causing huge economic losses. Research is focusing on detecting the problems in their early stages, thus saving farmers from financial hardship.

Chapter 7 provides a brief discussion on human emotion detection which reveals additional information. Several methods are discussed concerning the detection of human emotions from facial expressions. With the advancement in new technologies such as machine learning and deep learning, emotional features are extracted, showing the strength of both algorithms at pattern recognition and classification.

Chapter 8 elaborates on the significance of security systems and security measures employed in electronic commerce, discussing the essential requirements of e-transactions. Further, the chapter throws light on the various dimensions of security relating to e-commerce. Concepts such as secure SSL certificates, SHTTP and secure digital transmission as well as major strategies employed for combating fraud are discussed. The chapter wraps up with a review of e-commerce payment systems.

Chapter 9 reports on a survey that uses various techniques to detect, classify and quantify rice diseases. The authors stress the need for early detection of symptoms. Detection, classification and quantification are integrated into each technique’s algorithms.

Chapter 10 proposes a transfer learning-based multimodal convolutional denoising auto encoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with a better-quality score than by randomly initialised weights. The proposed work achieves a compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in cases of consuming multiple sensors. And the experimental result proves that the computation cost is lower in multimodal compression than in unimodal compression.

Chapter 11 discusses the popular contexts of metaheuristics and big data which are employed in current information technology. Later on, various key concepts of deep learning, deep neural networks and artificial neural networks are detailed. It critically analyses the difference between deep learning and machine learning while discussing the details of various metaheuristic algorithms such as genetic algorithms, practical swarm optimisation, etc.

Chapter 12 critically reviews communication strategies used on social networks from a multilingual perspective. This chapter first discusses communication strategies from different perspectives before making an in-depth review of communication strategies from a multilingual perspective. It then sheds light on the nature of communication on social networks as a multilingual and multicultural environment. Finally, it gives implications for educating second language communication in a multilingual world.



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  • The author's reference is not required

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