Applied Artificial Intelligence in Cybersecurity: Practices, Challenges, and Innovations

This Special Issue highlights the applications, practices, and theories of artificial intelligence in cybersecurity. In the past few decades, there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, etc.) for solving complex and intricate problems arising in cybersecurity. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this Special Issue is to bring forward thorough, in-depth, and well-focused developments of applied artificial intelligence technologies and their applications in cybersecurity, propose new approaches, and present innovative approaches in real facilities. 

AI can be both a blessing and a curse for cybersecurity. AI is being used both as a sword (i.e., in support of malicious attacks) and as a shield (to counter cybersecurity risks). With an additional complication: while the use of AI for defensive purposes faces several constraints, significantly as governments (and the European Union) move to regulate high-risk applications and promote the responsible use of AI, on the attack side, the most pernicious uses are multiplying, the cost of developing applications is plummeting, and the ‘attack surface’ is becoming denser every day, making any form of defense an uphill battle. Cybersecurity in IoT is a complex and challenging issue, as these devices are often designed with limited processing power and memory, making them vulnerable to various attacks.

Machine-learning and deep-learning techniques will make sophisticated cyber-attacks easier and allow for faster, better targeted, and more destructive attacks. The impact of AI on cybersecurity will likely expand the threat landscape, introduce new threats, and alter the typical characteristics of threats. In addition, other than introducing new and powerful vectors to carry out attacks, AI systems will also become increasingly subject to manipulation. Responsible AI refers to the ethical and transparent design, development, and deployment of artificial intelligence systems. As AI is increasingly integrated into various areas of our lives, the technology must be used in a way that is beneficial to society and aligns with human values.

AI will become increasingly indispensable to managing cyber threats in the cybersecurity sector. The market is expected to grow with a Compound Annual Growth Rate (CAGR) of 23.6% from 2020 to 2027 and to reach USD 46.3 billion by 2027. At the same time, adopting AI is not without risks in itself: more than 60% of companies adopting AI recognize cybersecurity risks generated by AI as the most relevant ones. The basic idea is to propose new approaches and ideas and to present applications of those innovative approaches in real-time environments.

Dr. Saurabh Agarwal
Dr. Tushar Bhardwaj
Dr. Upadhyay Himanshu
Dr. Leonel E. Lagos
Prof. Dr. Alexander Perez Pons
Guest Editors

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• cybersecurity, applied artificial intelligence, network forensics, machine learning, deep learning, anomaly detection, block chain, cybersecurity in IoT, deepfake, responsible AI, federated learning, AI trust, adversarial AI