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Feature Papers in Network Security and Privacy
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Feature Papers in Network Security and Privacy

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Security and Privacy".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 24598

Special Issue Editor

Dr. Jordi Mongay Batalla

E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: internet architecture and applications; multimedia; IoT; smart city; smart home; UAV; software defined networking; network function virtualization; 5G/6G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is a Special Issue of high-quality feature papers in order to give a broad overview in the field of Network Security and Privacy.

The scope of “Feature Papers in Network Security and Privacy, but is not limited to, the following items:

  • Security architectures of telecommunication systems;
  • Security in open Telco infrastructures;
  • Key agreement protocols and mechanisms;
  • Cryptography for advanced secure protocols;
  • Security of supporting ICT infrastructures;
  • ICT generic products security and privacy;
  • Confidentiality, integrity, and availability of information;
  • Quantum computing risks and challenges;
  • Security risk assessment and security assurance;

Regulation progress on security assessment.

Dr. Jordi Mongay Batalla
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. Journal of Sensor and Actuator Networks 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 2000 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

  • security and privacy
  • key agreement protocols
  • cryptography
  • security of ICT infrastructures
  • confidentiality
  • integrity and availability

Published Papers (7 papers)

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Editorial

Jump to: Research, Review

4 pages, 148 KiB  
Editorial
Featured Papers on Network Security and Privacy
by Jordi Mongay Batalla
J. Sens. Actuator Netw. 2024, 13(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan13010011 - 01 Feb 2024
Viewed by 1081
Abstract
There is an urgent need to introduce security-by-design in networks [...] Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)

Research

Jump to: Editorial, Review

14 pages, 814 KiB  
Article
Machine Learning-Based Detection for Unauthorized Access to IoT Devices
by Malak Aljabri, Amal A. Alahmadi, Rami Mustafa A. Mohammad, Fahd Alhaidari, Menna Aboulnour, Dorieh M. Alomari and Samiha Mirza
J. Sens. Actuator Netw. 2023, 12(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan12020027 - 20 Mar 2023
Cited by 4 | Viewed by 3146
Abstract
The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data’s integrity and confidentiality. Considering the dynamic [...] Read more.
The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data’s integrity and confidentiality. Considering the dynamic nature of the attacks, artificial intelligence (AI)-based techniques incorporating machine learning (ML) are promising techniques for identifying such attacks. However, the dataset being utilized features engineering techniques, and the kind of classifiers play significant roles in how accurate AI-based predictions are. Therefore, for the IoT environment, there is a need to contribute more to this context by evaluating different AI-based techniques on datasets that effectively capture the environment’s properties. In this paper, we evaluated various ML models with the consideration of both binary and multiclass classification models validated on a new dedicated IoT dataset. Moreover, we investigated the impact of different features engineering techniques including correlation analysis and information gain. The experimental work conducted on bagging, k-nearest neighbor (KNN), J48, random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP) models revealed that RF achieved the highest performance across all experiment sets, with a receiver operating characteristic (ROC) of 99.9%. Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
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18 pages, 330 KiB  
Article
Implementation of Elliptic Curves in the Polynomial Blom Key Pre-Distribution Scheme for Wireless Sensor Networks and Distributed Ledger Technology
by Siti Noor Farwina Mohamad Anwar Antony and Muhammad Fatihin Afiq Bahari
J. Sens. Actuator Netw. 2023, 12(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan12010015 - 09 Feb 2023
Cited by 3 | Viewed by 1501
Abstract
One of the challenges in securing wireless sensor networks (WSNs) is the key distribution; that is, a single shared key must first be known to a pair of communicating nodes before they can proceed with the secure encryption and decryption of the data. [...] Read more.
One of the challenges in securing wireless sensor networks (WSNs) is the key distribution; that is, a single shared key must first be known to a pair of communicating nodes before they can proceed with the secure encryption and decryption of the data. In 1984, Blom proposed a scheme called the symmetric key generation system as one method to solve this problem. Blom’s scheme has proven to be λ-secure, which means that a coalition of λ+1 nodes can break the scheme. In 2021, a novel and intriguing scheme based on Blom’s scheme was proposed. In this scheme, elliptic curves over a finite field are implemented in Blom’s scheme for the case when λ=1. However, the security of this scheme was not discussed. In this paper, we point out a mistake in the algorithm of this novel scheme and propose a way to fix it. The new fixed scheme is shown to be applicable for arbitrary λ. The security of the proposed scheme is also discussed. It is proven that the proposed scheme is also λ-secure with a certain condition. In addition, we also discuss the application of this proposed scheme in distributed ledger technology (DLT). Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
18 pages, 5519 KiB  
Article
Homoglyph Attack Detection Model Using Machine Learning and Hash Function
by Abdullah M. Almuhaideb, Nida Aslam, Almaha Alabdullatif, Sarah Altamimi, Shooq Alothman, Amnah Alhussain, Waad Aldosari, Shikah J. Alsunaidi and Khalid A. Alissa
J. Sens. Actuator Netw. 2022, 11(3), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11030054 - 16 Sep 2022
Cited by 3 | Viewed by 3914
Abstract
Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This [...] Read more.
Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection. Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
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26 pages, 3889 KiB  
Article
Efficient Privacy-Preserving and Secure Authentication for Electric-Vehicle-to-Electric-Vehicle-Charging System Based on ECQV
by Abdullah M. Almuhaideb and Sammar S. Algothami
J. Sens. Actuator Netw. 2022, 11(2), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11020028 - 09 Jun 2022
Cited by 9 | Viewed by 3383
Abstract
The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest [...] Read more.
The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts. Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
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Review

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50 pages, 1152 KiB  
Review
AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions
by Reem A. Alzahrani and Malak Aljabri
J. Sens. Actuator Netw. 2023, 12(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan12010004 - 31 Dec 2022
Cited by 6 | Viewed by 8430
Abstract
Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements [...] Read more.
Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
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23 pages, 1550 KiB  
Review
Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
by Kealan Mannix, Aengus Gorey, Donna O’Shea and Thomas Newe
J. Sens. Actuator Netw. 2022, 11(3), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11030043 - 08 Aug 2022
Cited by 4 | Viewed by 2285
Abstract
Over the past decade, new technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as the advent of [...] Read more.
Over the past decade, new technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as the advent of industry 4.0, the internet of things, cloud technologies and smart homes and cities. These new and exciting areas are poised to have significant advantages for society; they can increase the efficiency of many systems and increase the quality of life of people. However, these emerging technologies can potentially have downsides, if used incorrectly or maliciously by bad entities. The rise of the widespread use of sensor networks to allow the mentioned systems to function has brought with it many security vulnerabilities that conventional “hard security” measures cannot mitigate. It is for this reason that a new “soft security” approach is being taken in conjunction with the conventional security means. Trust models offer an efficient way of mitigating the threats posed by malicious entities in networks that conventional security methods may not be able to combat. This paper discusses the general structure of a trust model, the environments they are used in and the attack types they are used to defend against. The work aims to provide a comprehensive review of the wide assortment of trust parameters and methods used in trust models. The work discusses which environments and network types each of these parameters and calculation methods would be suited to. Finally, a design study is provided to demonstrate how a trust model design will differ between two different industry 4.0 networks. Full article
(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)
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