Abstract
The cyberspace plays a major role in the rapid digital transformation of business due to its extensive interconnectivity and cyber potential. With the integration of automation and information technology, the quality, productivity and optimization of enterprise workflow are achieved. Due to rapid growth in digital technologies such as cloud computing, Internet of things, big data, artificial intelligence and machine learning, managing and processing a huge amount of digital data has been a complex process. This has increased the risk in data security and challenges in digital transformation. This paper provides a comprehensive state-of-the-art summary of cybersecurity in the digital transformation and aims at identifying security risks associated with these emerging technologies along with providing solution to overcome these challenges.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Angelo C, Mariangela L, Marianna L (2020) Cybersecurity in the context of industry 4.0: a structured classification of critical assets and business impacts. Comput Ind 114:103165
Mariia P, Deniz K, Rob L (2022) Digital business model configurations in the travel industry. Tour Manage 88:104408
Paul F, Michael W, Deborah R (2021) A principlist framework for cybersecurity ethics. Comput Secur 109:102382
Russell SJ, Norvig P (1995) Artificial intelligence: a modern approach. Prentice Hall, New Jersey 0-13-103805-2
Giuseppe C, Damian AT, Willem-JanVan DH (2021) Cybercrime threat intelligence: a systematic multi-vocal literature review. Comput Sec 105:102258
Giuseppe C, Damian AT, Willem-JanVan DH (2021) Cybercrime threat intelligence: a systematic multi-vocal literature review. Comput Sec 105:102258
Nayan C, Chitvan M, Singh AS (2017) Risk for big data in the cloud. In: International conference on computing, communication and automation (ICCCA), IEEE, Greater Noida, India, 5–6 May 2017
Nuno M, Jose MC, Tiago C, Pedro HA (2020) Adversarial machine learning applied to ıntrusion and malware scenarios: a systematic review. IEEE Access 8:35403–35419
Toya A, Ishan K, Annamalai A, Chouikha MF (2021) Efficacy of machine learning-based classifiers for binary and multi-class network ıntrusion detection. In: International conference on automatic control and ıntelligent systems (I2CACIS), IEEE, Shah Alam, Malaysia, 26–26 June 2021
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swain, A., Swain, K.P., Pattnaik, S.K., Samal, S.R., Das, J.K. (2022). Cybersecurity in Digital Transformations. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_26
Download citation
DOI: https://doi.org/10.1007/978-981-19-0825-5_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0824-8
Online ISBN: 978-981-19-0825-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)