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Future Internet
Special Issues
Cybersecurity in the Era of AI
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Cybersecurity in the Era of AI

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 16091

Special Issue Editors

Dr. Mazdak Zamani

E-Mail Website
Guest Editor
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
Interests: cybersecurity; data science; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Rohit Tanwar

E-Mail Website
Guest Editor
School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
Interests: network security and nature based optimization
Dr. Touraj Khodadadi

E-Mail Website
Guest Editor
Department of Information Technology, Malaysia University of Science and Technology, Petaling Jaya, Malaysia
Interests: security; information security; IT security; computer security; network security; computer networks security; cyber security; cloud computing; computer networking; network communication

Special Issue Information

Dear Colleagues,

AI experiences an extensive variety of cyberattacks itself. These kinds of attacks tend to deceive the machine learning task and ultimately impact decision outcome. This finds the scope of using cybersecurity to ensure the privacy of sensitive information in the machine learning process. The coordination of these two technologies would yield secure and smarter learning. Better identification of unknown threats, improved control for vulnerability management, and smarter as well as secure authentication are some of the examples that would be possible with this fusion of technologies.

The more attacks are sophisticated, the more advanced techniques need to be detect them. Artificial intelligence techniques are playing a key role in identification of the risks and attacks. These detections occur either before the attacks or after.

Dr. Mazdak Zamani
Dr. Rohit Tanwa
Dr. Touraj Khodadadi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cybersecurity cyberattack
  • machine learning
  • artificial intelligence
  • vulnerability assessment
  • risk assessment
  • risk identification

Published Papers (3 papers)

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Research

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20 pages, 679 KiB  
Article
A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
by Soumya Prakash Otta, Subhrakanta Panda, Maanak Gupta and Chittaranjan Hota
Future Internet 2023, 15(4), 146; https://doi.org/10.3390/fi15040146 - 10 Apr 2023
Cited by 4 | Viewed by 4340
Abstract
The unauthorized usage of various services and resources in cloud computing is something that must be protected against. Authentication and access control are the most significant concerns in cloud computing. Several researchers in this field suggest numerous approaches to enhance cloud authentication towards [...] Read more.
The unauthorized usage of various services and resources in cloud computing is something that must be protected against. Authentication and access control are the most significant concerns in cloud computing. Several researchers in this field suggest numerous approaches to enhance cloud authentication towards robustness. User names and associated passwords have been a common practice for long as Single Factor Authentication. However, advancements in the speed of computing and the usage of simple methods, starting from the Brute Force technique to the implementation of advanced and efficient crytographic algorithms, have posed several threats and vulnerabilities for authentication systems, leading to the degradation of their effectiveness. Multi-factor authentication has emerged as a robust means of securing the cloud using simultaneous and multiple means of authentication factors. This employs multiple levels of cascaded authentication checks. This paper covers an extensive and systematic survey of various factors towards their adoption and suitability for authentication for multi-factor authentication mechanisms. The inference drawn from the survey is in terms of arriving at a unique authentication factor that does not require any additional, specialized hardware or software for multi-factor authentication. Such authentication also uses the distinct biometric characteristics of the concerned user in the process. This arrangement augments the secured and robust user authentication process. The mechanism is also assessed as an effective means against impersonation attacks. Full article
(This article belongs to the Special Issue Cybersecurity in the Era of AI)
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18 pages, 2258 KiB  
Article
SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning
by Yuting Guan, Junjiang He, Tao Li, Hui Zhao and Baoqiang Ma
Future Internet 2023, 15(4), 133; https://doi.org/10.3390/fi15040133 - 30 Mar 2023
Cited by 5 | Viewed by 2734
Abstract
SQL injection is a highly detrimental web attack technique that can result in significant data leakage and compromise system integrity. To counteract the harm caused by such attacks, researchers have devoted much attention to the examination of SQL injection detection techniques, which have [...] Read more.
SQL injection is a highly detrimental web attack technique that can result in significant data leakage and compromise system integrity. To counteract the harm caused by such attacks, researchers have devoted much attention to the examination of SQL injection detection techniques, which have progressed from traditional signature-based detection methods to machine- and deep-learning-based detection models. These detection techniques have demonstrated promising results on existing datasets; however, most studies have overlooked the impact of adversarial attacks, particularly black-box adversarial attacks, on detection methods. This study addressed the shortcomings of current SQL injection detection techniques and proposed a reinforcement-learning-based black-box adversarial attack method. The proposal included an innovative vector transformation approach for the original SQL injection payload, a comprehensive attack-rule matrix, and a reinforcement-learning-based method for the adaptive generation of adversarial examples. Our approach was evaluated on existing web application firewalls (WAF) and detection models based on machine- and deep-learning methods, and the generated adversarial examples successfully bypassed the detection method at a rate of up to 97.39%. Furthermore, there was a substantial decrease in the detection accuracy of the model after multiple attacks had been carried out on the detection model via the adversarial examples. Full article
(This article belongs to the Special Issue Cybersecurity in the Era of AI)
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Review

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37 pages, 2254 KiB  
Review
Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods
by Tehseen Mazhar, Hafiz Muhammad Irfan, Sunawar Khan, Inayatul Haq, Inam Ullah, Muhammad Iqbal and Habib Hamam
Future Internet 2023, 15(2), 83; https://doi.org/10.3390/fi15020083 - 19 Feb 2023
Cited by 28 | Viewed by 8457
Abstract
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of [...] Read more.
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur. Full article
(This article belongs to the Special Issue Cybersecurity in the Era of AI)
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