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Future Internet
Special Issues
Artificial Intelligence and IoT Security: Opportunities and Challenges
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Artificial Intelligence and IoT Security: Opportunities and Challenges

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3126

Special Issue Editor

Dr. Nikos Fotiou

E-Mail Website
Guest Editor
Mobile Multimedia Laboratory, Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business, 104 34 Athens, Greece
Interests: access control; blockchain technologies; cryptography; information-centric networking; IoT; privacy; security; web technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is becoming an integral part of our lives. Nevertheless, security and privacy threats still prevent us from harvesting the full potential of IoT systems. IoT applications merge the physical with the cyber world, creating new challenges and threats. At the same time, IoT communications are susceptible to traffic analysis, even when encryption is used, and IoT devices are not well protected against malicious actors. Artificial intelligence (AI) provides the potential for innovative, intelligent solutions to the thorny security problems of IoT. However, at the same time, AI enables new and sophisticated security and privacy attacks, making IoT security even more challenging.         

This Special Issue aims to consolidate recent research in the area of IoT security, focusing on AI-based attacks and defenses. This Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas:

  • IoT security policy verification and testing based on AI;
  • AI-enhanced access control;
  • Security analytics for IoT networks using machine learning;
  • Learning methods for IoT security;
  • IoT network intrusion detection based on AI;
  • AI-enabled attacks and defenses on IoT systems;
  • AI-based privacy-enhancing technologies for IoT systems;
  • AI-assisted fingerprinting of IoT devices;
  • Vertical IoT applications of AI-assisted security solutions;
  • Experiments and datasets on AI-assisted IoT security;
  • IoT-assisted, AI-based security solutions;
  • IoT security management using AI.

Dr. Nikos Fotiou
Guest Editor

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

  • access control
  • attacks on IoT networks
  • AI applications
  • experimentation
  • privacy-enhancing technologies
  • security analytics
  • security defenses
  • smart detection

Published Papers (2 papers)

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Research

18 pages, 4521 KiB  
Article
PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
by Ernesto Cadena Muñoz, Gustavo Chica Pedraza, Rafael Cubillos-Sánchez, Alexander Aponte-Moreno and Mónica Espinosa Buitrago
Future Internet 2023, 15(6), 202; https://doi.org/10.3390/fi15060202 - 31 May 2023
Viewed by 1326
Abstract
The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques [...] Read more.
The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU. Full article
(This article belongs to the Special Issue Artificial Intelligence and IoT Security: Opportunities and Challenges)
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17 pages, 456 KiB  
Article
Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
by Guilherme Yukio Sakurai, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
Future Internet 2023, 15(5), 169; https://doi.org/10.3390/fi15050169 - 29 Apr 2023
Cited by 2 | Viewed by 1364
Abstract
The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in [...] Read more.
The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance. Full article
(This article belongs to the Special Issue Artificial Intelligence and IoT Security: Opportunities and Challenges)
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