Cybersecurity and Data Science

doi:10.3390/books978-3-0365-6907-9

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Cybersecurity and Data Science

Edited by

March 2023

320 pages

ISBN978-3-0365-6906-2 (Hardback)
ISBN978-3-0365-6907-9 (PDF)

https://doi.org/10.3390/books978-3-0365-6907-9

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This is a Reprint of the Special Issue Cybersecurity and Data Science that was published in

Computer Science & Mathematics

Engineering

Summary

The reprint focuses on the latest research in cybersecurity and data science. Digital transformation turns data into the new oil, so the increasing availability of big data, structured and unstructured datasets, raises new challenges in cybersecurity, efficient data processing and knowledge extraction. The field of cybersecurity and data science fuels the data-driven economy. Innovations in this field require strong foundations in mathematics, statistics, machine learning and information security. The unprecedented increase in the availability of data in many fields of science and technology (e.g., genomic data, data from industrial environments, network traffic, streaming media, sensory data of smart cities, and social network data) ask for new methods and solutions for data processing, information extraction and decision support. This stimulates the development of new methods of data analysis, including those adapted to the analysis of new data structures and the growing volume of data. The papers included in this reprint discuss various topics ranging from cyberattacks, steganography, anomaly detection, evaluation of the attacker skills, modelling of the threats, and wireless security evaluation, as well as artificial intelligence, machine learning, and deep learning. Given this diversity of topics the book represents a valuable reference for researchers in cybersecurity security and data science.

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Hardback

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Keywords

steganography; network security; steganography detection; steganalysis; machine learning; big data; IoT; pattern mining; wireless communications; covert channel; steganography; steganalysis; dirty constellation; wireless postmodulation steganography; phase drift; drift correction modulation; undetectability; security; quadrature amplitude modulation; spam; phishing; classification; augmented dataset; multi-language emails; cybersecurity; IoT; data protection; SoC; threat agents; motivation; opportunity; capability; user profiling; implicit; modeling; real-time user monitoring; complexity threat agent; threat assessment; network traffic analysis; convolutional neural networks; machine learning; network traffic images; visualization of traffic; classifiers; e-mail; ham; machine learning; spam; cybersecurity; data science; machine learning; datasets; cyber threats modeling; multi-agent systems; cyber deception; cybersecurity; pseudorandom sequences generators; prime numbers; additive Fibonacci generator; statistical characteristics; android device; BrainShield; hybrid model; machine learning; malware detection; Omnidroid; steganography; machine learning; image processing; BOSS database; ensemble classifier; deep learning; steganalysis; stegomalware; traffic analysis; network probe; hash function; SHA-3; FPGA; cognitive security; cybersecurity; cyberattacks; game software; threat matrix computing; evaluation function; data modeling; cybersecurity; authentication; bit template; information-processing electronic device; Poisson pulse sequences generators; n/a