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Future Internet
Special Issues
Mobile and Wireless Network Security and Privacy
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Mobile and Wireless Network Security and Privacy

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 14634

Special Issue Editors

Dr. Rajan Shankaran

E-Mail Website
Guest Editor
Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
Interests: wireless and mobile networks security; sensor networks security; QoS and energy-aware routing; cognitive radio networks; security in mobile ad hoc networks; denial of service attacks in Internet of Things; trust management in ad hoc/sensor networks; key management in ad hoc/sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Ni

E-Mail Website
Guest Editor
Cybernetics Group, Cyber-Physical System (CPS) Program, CSIRO, Canberra 2601, Australia
Interests: signal processing; communication network; security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of mobile computing is the merger of advances in computing and communications with the aim of providing a seamless and ubiquitous computing environment for mobile users. The need for information anywhere and at any time has been a driving force for the increasing growth in mobile networks and devices. Over the years, there has been a rapid proliferation of a plethora of wireless networking technologies ranging from fifth-generation cellular networks (5-G standards), Wi-Fi (802.11), PAN (802.15), MANETS, Cognitive radio networks to the Internet of Things. Moreover, in the recent past, there has been an exponential improvement in the capacity and performance of such mobile and wireless technologies, which has led to a range of information and services being offered over such platforms. Without doubt, the ability to communicate continuously and effectively is proving to be critical for both everyday life and business productivity. However, security remains the chief concern for mobile and wireless networks. Amid a rapidly changing and constantly evolving cyber threat landscape, individuals and organisations are struggling to best adapt to this new way of living and working. There is not only a need to preserve the integrity, confidentiality and availability of resources and the network, but it is also crucial to safeguard users’ privacy and anonymity.

This Special Issue will provide a broad platform to showcase novel research on all aspects of security and privacy in state-of-the-art mobile and wireless networks along with trade-off between security and performance. Submissions of scientific results from experts in academia and industry worldwide are strongly encouraged. Topics of interest include but are not limited to the following:

  • Lightweight and energy-aware privacy-enhancing cryptographic techniques and algorithms for mobile and wireless networks;
  • Key management and public key infrastructure in mobile and wireless computing;
  • Prevention of DoS and DDoS attacks in mobile and wireless networks;
  • Reasoning about security and privacy in mobile and wireless networks;
  • Security, privacy and anonymity in mobile and wireless computing;
  • Economics of information security and privacy in wireless and mobile networks;
  • Authentication, auditing and accountability in mobile and wireless networks;
  • Security protocols and architectures for WLANs, PANs, 5G and next-generation mobile networks;
  • Security and privacy features in wireless wearable devices and implants;
  • Security and privacy in resource-starved wireless devices and networks;
  • Security and trust in cognitive radio networks;
  • Location privacy and anonymity;
  • Fraud analysis, intrusion and anomaly detection;
  • Mobile ad hoc and sensor network security;
  • Security and privacy issues in opportunistic and delay-tolerant networks;
  • Security and privacy in pervasive computing;
  • Trust establishment, negotiation and management.

Dr. Rajan Shankaran
Dr. Wei Ni
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.

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Published Papers (2 papers)

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Research

11 pages, 460 KiB  
Article
Security Challenges for Light Emitting Systems
by Louiza Hamada, Pascal Lorenz and Marc Gilg
Future Internet 2021, 13(11), 276; https://doi.org/10.3390/fi13110276 - 28 Oct 2021
Cited by 2 | Viewed by 1963
Abstract
Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user’s data from eavesdroppers. In the [...] Read more.
Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user’s data from eavesdroppers. In the literature, keyless security techniques have been developed to offer security for VLC. Even though these techniques provide strong security against eavesdroppers, they are difficult to deploy. Key generation algorithms are critical for securing wireless connections. Nonetheless, in many situations, the typical key generation methods may be quite complicated and costly. They consume scarce resources, such as bandwidth. In this paper, we propose a novel key extraction procedure that uses error-correcting coding and one time pad (OTP) to improve the security of VLC networks and the validity of data. This system will not have any interference problems with other devices. We also explain error correction while sending a message across a network, and suggest a change to the Berlekamp–Massey (BM) algorithm for error identification and assessment. Because each OOK signal frame is encrypted by a different key, the proposed protocol provides high physical layer security; it allows for key extraction based on the messages sent, so an intruder can never break the encryption system, even if the latter knows the protocol with which we encrypted the message; our protocol also enables for error transmission rate correction and bit mismatch rates with on-the-fly key fetch. The results presented in this paper were performed using MATLAB. Full article
(This article belongs to the Special Issue Mobile and Wireless Network Security and Privacy)
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18 pages, 516 KiB  
Article
Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks
by Abdulsalam O. Alzahrani and Mohammed J. F. Alenazi
Future Internet 2021, 13(5), 111; https://doi.org/10.3390/fi13050111 - 28 Apr 2021
Cited by 157 | Viewed by 11869
Abstract
Software-defined Networking (SDN) has recently developed and been put forward as a promising and encouraging solution for future internet architecture. Managed, the centralized and controlled network has become more flexible and visible using SDN. On the other hand, these advantages bring us a [...] Read more.
Software-defined Networking (SDN) has recently developed and been put forward as a promising and encouraging solution for future internet architecture. Managed, the centralized and controlled network has become more flexible and visible using SDN. On the other hand, these advantages bring us a more vulnerable environment and dangerous threats, causing network breakdowns, systems paralysis, online banking frauds and robberies. These issues have a significantly destructive impact on organizations, companies or even economies. Accuracy, high performance and real-time systems are essential to achieve this goal successfully. Extending intelligent machine learning algorithms in a network intrusion detection system (NIDS) through a software-defined network (SDN) has attracted considerable attention in the last decade. Big data availability, the diversity of data analysis techniques, and the massive improvement in the machine learning algorithms enable the building of an effective, reliable and dependable system for detecting different types of attacks that frequently target networks. This study demonstrates the use of machine learning algorithms for traffic monitoring to detect malicious behavior in the network as part of NIDS in the SDN controller. Different classical and advanced tree-based machine learning techniques, Decision Tree, Random Forest and XGBoost are chosen to demonstrate attack detection. The NSL-KDD dataset is used for training and testing the proposed methods; it is considered a benchmarking dataset for several state-of-the-art approaches in NIDS. Several advanced preprocessing techniques are performed on the dataset in order to extract the best form of the data, which produces outstanding results compared to other systems. Using just five out of 41 features of NSL-KDD, a multi-class classification task is conducted by detecting whether there is an attack and classifying the type of attack (DDoS, PROBE, R2L, and U2R), accomplishing an accuracy of 95.95%. Full article
(This article belongs to the Special Issue Mobile and Wireless Network Security and Privacy)
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