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AI-Enabled Security Technologies
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AI-Enabled Security Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 8772

Special Issue Editors

Prof. Dr. Jeong Hyun Yi

E-Mail Website
Guest Editor
School of Software, Soongsil University, Seoul 06978, Korea
Interests: cybersecurity; AI security; IoT security; malware analysis; reverse engineering; software protection
Prof. Dr. Gail-Joon Ahn

E-Mail Website
Guest Editor
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, AZ 85281, USA
Interests: security analytics and big data driven security intelligence; vulnerability and risk management; access control and security architecture for distributed systems; identity and privacy management; cyber crime analysis; security-enhanced computing platforms; and formal models for computer security

Special Issue Information

Dear Colleagues,

With the rapid development of the IT technology environment, various challenges are emerging in the existing cybersecurity field. Advanced breaches are constantly occurring, and malicious codes that are difficult to detect with existing anti-virus products are steadily appearing. It cannot be solved with contemporary limited security personnel and technology, and it is necessary to introduce new technology that can respond automatically and efficiently. In other words, it is time that artificial intelligence (AI) technology should be applied to the cybersecurity field.

Since AI technology is operating in an existing computing environment, attacks that hijack AI or interfere with AI services may not differ significantly from existing information security issues. Problems such as hacking an autonomous vehicle, attacking a medical diagnosis/surgery system, and hacking a military robot, including an unmanned aerial vehicle, can cause serious damage. Moreover, if you attack AI by polluting the learning data acquired by numerous sensors or finding weaknesses in the learning model, it may cause artificial intelligence to malfunction and cause unimaginable damage. Thus, it is essential to develop security technologies that are enabled with AI.

Hence, this Special Issue will bring together researchers, practitioners, policymakers, and hardware and software developers in cybersecurity to explore the latest understanding and advancements in the security and privacy of AI-enabled systems, applications, and services. This Special Issue will provide insights into the discussion of major research challenges and achievements covering various topics of interest.

Potential topics include, but are not limited to the following:

  • AI-enabled user authentication
  • AI-enabled malware detection and recovery
  • AI-enabled wireless security
  • AI-enabled 5G security
  • AI-enabled IoT security
  • AI-enabled vehicular security
  • AI-enabled digital forensics
  • AI-enabled cybersecurity attacks and defense
  • AI-enabled mobile security
  • AI-enabled OS/middleware security
  • AI-enabled application security

Prof. Dr. Jeong Hyun Yi
Prof. Dr. Gail-Joon Ahn
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Published Papers (2 papers)

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Research

15 pages, 5719 KiB  
Article
DDosTC: A Transformer-Based Network Attack Detection Hybrid Mechanism in SDN
by Haomin Wang and Wei Li
Sensors 2021, 21(15), 5047; https://doi.org/10.3390/s21155047 - 26 Jul 2021
Cited by 28 | Viewed by 4726
Abstract
Software-defined networking (SDN) has emerged in recent years as a form of Internet architecture. Its scalability, dynamics, and programmability simplify the traditional Internet structure. This architecture realizes centralized management by separating the control plane and the data-forwarding plane of the network. However, due [...] Read more.
Software-defined networking (SDN) has emerged in recent years as a form of Internet architecture. Its scalability, dynamics, and programmability simplify the traditional Internet structure. This architecture realizes centralized management by separating the control plane and the data-forwarding plane of the network. However, due to this feature, SDN is more vulnerable to attacks than traditional networks and can cause the entire network to collapse. DDoS attacks, also known as distributed denial-of-service attacks, are the most aggressive of all attacks. These attacks generate many packets (or requests) and ultimately overwhelm the target system, causing it to crash. In this article, we designed a hybrid neural network DDosTC structure, combining efficient and scalable transformers and a convolutional neural network (CNN) to detect distributed denial-of-service (DDoS) attacks on SDN, tested on the latest dataset, CICDDoS2019. For better verification, several experiments were conducted by dividing the dataset and comparisons were made with the latest deep learning detection algorithm applied in the field of DDoS intrusion detection. The experimental results show that the average AUC of DDosTC is 2.52% higher than the current optimal model and that DDosTC is more successful than the current optimal model in terms of average accuracy, average recall, and F1 score. Full article
(This article belongs to the Special Issue AI-Enabled Security Technologies)
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33 pages, 731 KiB  
Article
S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information
by Juan Manuel Espín López, Alberto Huertas Celdrán, Javier G. Marín-Blázquez, Francisco Esquembre and Gregorio Martínez Pérez
Sensors 2021, 21(11), 3765; https://doi.org/10.3390/s21113765 - 28 May 2021
Cited by 5 | Viewed by 3055
Abstract
Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates [...] Read more.
Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community. Full article
(This article belongs to the Special Issue AI-Enabled Security Technologies)
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