Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
AI-Enabled Security and Privacy Mechanisms for IoT
share announcement

AI-Enabled Security and Privacy Mechanisms for IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2020) | Viewed by 27643

Special Issue Editor

Dr. Mazdak Zamani

E-Mail Website
Guest Editor
School of Business and Information Sciences, Felician University, 1 Felician Way, Rutherford, NJ 07070, USA
Interests: networks; artificial Intelligence; computer Science & engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is intended to create a hyper network of connected sensors that communicate with others to deliver various services to end users. IoT offers services for humans to control and manage operational objects in different areas such as healthcare systems, smart cities, agriculture systems, etc. Due to the huge numbers of heterogeneous networks, sensors, platforms, and application working under IoT eco-system, there is a critical need for sophisticated artificial intelligence (AI) mechanisms to enhance the reliability of IoT networks. On the other hand, recent advances in artificial intelligence benefits various research areas including cloud computing, big data, image processing, etc. Accordingly, IoT also benefits from AI being improved in different domains. Despite ongoing development, security and privacy problems are increased as AI mechanisms require accessing more data from embedded sensors to get better results.

This Special Issue is to solicit ongoing research studies in the domain of AI-enabled security and privacy mechanisms in IoT. Different methods that applied AI mechanisms to improve the security and privacy of IoT will be suitable for this Special Issue. Both experimental and theoretical studies related to the topic of this Special Issue are encouraged.
The research areas for this Special Issue include but are not limited to the following domains:

  • AI-enabled security mechanisms for IoT;
  • Secure framework and platform for IoT;
  • AI-enabled Intrusion detection systems;
  • Privacy protection mechanisms for IoT;
  • Authentication protocol for IoT based on AI;
  • Intelligent application to secure IoT systems;

Security, privacy, and trust protocol for IoT.

Technical Program Committee Members:

1. Dr. Mojtaba Alizadeh, Lorestan University
2. Dr. Bharanidharan Shanmugam, Charles Darwin University

Dr. Mazdak Zamani
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. Electronics 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 2400 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

  • Artificial intelligence
  • Security
  • Privacy
  • AI-enabled mechanisms
  • IoT
  • Machine learning

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

27 pages, 2583 KiB  
Article
Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems
by Chao Liang, Bharanidharan Shanmugam, Sami Azam, Asif Karim, Ashraful Islam, Mazdak Zamani, Sanaz Kavianpour and Norbik Bashah Idris
Electronics 2020, 9(7), 1120; https://doi.org/10.3390/electronics9071120 - 10 Jul 2020
Cited by 90 | Viewed by 7914
Abstract
With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses [...] Read more.
With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment. Full article
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
►▼ Show Figures

Figure 1

15 pages, 494 KiB  
Article
Robustness and Unpredictability for Double Arbiter PUFs on Silicon Data: Performance Evaluation and Modeling Accuracy
by Meznah A. Alamro, Khalid T. Mursi, Yu Zhuang, Ahmad O. Aseeri and Mohammed Saeed Alkatheiri
Electronics 2020, 9(5), 870; https://doi.org/10.3390/electronics9050870 - 24 May 2020
Cited by 12 | Viewed by 3014
Abstract
Classical cryptographic methods that inherently employ secret keys embedded in non-volatile memory have been known to be impractical for limited-resource Internet of Things (IoT) devices. Physical Unclonable Functions (PUFs) have emerged as an applicable solution to provide a keyless means for secure authentication. [...] Read more.
Classical cryptographic methods that inherently employ secret keys embedded in non-volatile memory have been known to be impractical for limited-resource Internet of Things (IoT) devices. Physical Unclonable Functions (PUFs) have emerged as an applicable solution to provide a keyless means for secure authentication. PUFs utilize inevitable variations of integrated circuits (ICs) components, manifest during the fabrication process, to extract unique responses. Double Arbiter PUFs (DAPUFs) have been recently proposed to overcome security issues in XOR PUF and enhance the tolerance of delay-based PUFs against modeling attacks. This paper provides comprehensive risk analysis and performance evaluation of all proposed DAPUF designs and compares them with their counterparts from XOR PUF. We generated different sets of real challenge–response pairs CRPs from three FPGA hardware boards to evaluate the performance of both DAPUF and XOR PUF designs using special-purpose evaluation metrics. We show that none of the proposed designs of DAPUF is strictly preferred over XOR PUF designs. In addition, our security analysis using neural network reveals the vulnerability of all DAPUF designs against machine learning attacks. Full article
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
►▼ Show Figures

Figure 1

15 pages, 3785 KiB  
Article
PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups
by Saman Shojae Chaeikar, Azizah Abdul Manaf, Ala Abdulsalam Alarood and Mazdak Zamani
Electronics 2020, 9(4), 615; https://doi.org/10.3390/electronics9040615 - 05 Apr 2020
Cited by 11 | Viewed by 2714
Abstract
Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed [...] Read more.
Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed from input space to feature space, and then they are classified. However, carrying out this transformation is not always practical, and the process itself increases the cost of training and prediction. To address these problems, this paper puts forward an SVM kernel, called polygonal fuzzy weighted or PFW, which effectively classifies data without space transformation, even if the groups in question are not linearly separable and have overlapping areas. This kernel is based on Gaussian data distribution, standard deviation, the three-sigma rule and a polygonal fuzzy membership function. A comparison of our PFW, radial basis function (RBF) and conventional linear kernels in identical experimental conditions shows that PFW produces a minimum of 26% higher classification accuracy compared with the linear kernel, and it outperforms the RBF kernel in two-thirds of class labels, by a minimum of 3%. Moreover, Since PFW runs within the original feature space, it involves no additional computational cost. Full article
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
►▼ Show Figures

Figure 1

21 pages, 12868 KiB  
Article
Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested Environment
by Junren Luo, Wanpeng Zhang, Wei Gao, Zhiyong Liao, Xiang Ji and Xueqiang Gu
Electronics 2020, 9(1), 5; https://doi.org/10.3390/electronics9010005 - 18 Dec 2019
Cited by 2 | Viewed by 2034
Abstract
Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private [...] Read more.
Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches. Full article
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
►▼ Show Figures

Figure 1

Review

Jump to: Research

27 pages, 1960 KiB  
Review
A Systematic Literature Review on Privacy by Design in the Healthcare Sector
by Farida Habib Semantha, Sami Azam, Kheng Cher Yeo and Bharanidharan Shanmugam
Electronics 2020, 9(3), 452; https://doi.org/10.3390/electronics9030452 - 07 Mar 2020
Cited by 31 | Viewed by 11255
Abstract
In this digital age, we are observing an exponential proliferation of sophisticated hardware- and software-based solutions that are able to interact with the users at almost every sensitive aspect of our lives, collecting and analysing a range of data about us. These data, [...] Read more.
In this digital age, we are observing an exponential proliferation of sophisticated hardware- and software-based solutions that are able to interact with the users at almost every sensitive aspect of our lives, collecting and analysing a range of data about us. These data, or the derived information out of it, are often too personal to fall into unwanted hands, and thus users are almost always wary of the privacy of such private data that are being continuously collected through these digital mediums. To further complicate the issue, the infringement cases of such databanks are on a sharp rise. Several frameworks have been devised in various parts of the globe to safeguard the issue of data privacy; in parallel, constant research is also being conducted on closing the loopholes within these frameworks. This study aimed to analyse the contemporary privacy by design frameworks to identify the key limitations. Seven contemporary privacy by design frameworks were examined in-depth in this research that was based on a systematic literature review. The result, targeted at the healthcare sector, is expected to produce a high degree of fortification against data breaches in the personal information domain. Full article
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop