Privacy and Security Landscape and Challenges beyond COVID-19

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 10591

Special Issue Editors


E-Mail Website
Guest Editor
Cardiff School of Technologies (CST), Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Interests: Quality of Experience (QoE); multimeida; security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Network Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: data privacy; cybersecurity; computer networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Interests: security; wireless sensor networks; vehicular networks; internet of things; privacy; trust management

E-Mail Website
Guest Editor
KIT, Fakultät für Informatik, Am Fasanengarten 5 Geb 50.34, 76131 Karlsruhe, Germany
Interests: information security and privacy; identity management, authentication, and usable security

Special Issue Information

Dear Colleagues,

COVID-19 has forced many of us to work from home (home working), learn online (home schooling), shop online and many more. It is envisaged that these trends (new normal) will remain post COVID-19 as well. This new normal will come at a price, especially with respect to privacy and security challenges. At one point during the pandemic, the WHO called the situation as an Infodemic due to the increased look for information. For instance, it is well documented that cyber criminals exploited the opportunity to attack governments, organizations (e.g., healthcare facilities) and public in general due to the increased Internet presence with COVID-19. There were reported cyber-attacks (e.g., Phishing) targeting Coronavirus vaccine research, development and distributions. The local and nation-wide contact tracing efforts also came under heavy criticism due to privacy and security concerns. Even though these efforts were necessary to control the spread of the virus, the individuals’ rights for their personal information have come under more scrutiny than ever before. Some even suggest to revisit the data protection laws and regulations (e.g., GDPR) in the wake of COVID-19. While it is necessary to address privacy and security concerns during COVID-19, it is also required to proactively address the threat landscape post-COVID-19. These new approaches need new paradigm shifts since we couldn’t expect the world to behave in the same manner in comparison to pre-COVID-19. Therefore it is necessary to anticipate, risk assess and mitigate privacy and security threats of new normal in post-COVID-19.

Original contributions showing practical approaches are also welcome.

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

  • Privacy and security challenges during and beyond COVID-19
  • Contact tracing vs privacy/security
  • Post-COVID privacy and security challenges for working from home
  • Secure Future Internet
  • Post-COVID privacy and security challenges for home schooling
  • Secure internet technologies
  • Privacy Enhancing Technologies (PET)
  • Next generation access control technologies
  • Revisiting data protection laws and regulations to suit post-COVID-19
  • Intersection between machine learning and security & privacy
  • Data anonymization
  • Security and privacy issues in federated learning
  • Privacy issues in geo-located services
  • Privacy-preserving big data analysis in healthcare applications

Dr. Chaminda Hewage
Dr. Jordi Forné
Dr. Riaz Shaikh
Dr. Patricia Arias Cabarcos
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.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

36 pages, 6372 KiB  
Article
Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME)
by Nisha Rawindaran, Ambikesh Jayal, Edmond Prakash and Chaminda Hewage
Future Internet 2021, 13(8), 186; https://doi.org/10.3390/fi13080186 - 21 Jul 2021
Cited by 12 | Viewed by 5671
Abstract
Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions [...] Read more.
Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs. Full article
(This article belongs to the Special Issue Privacy and Security Landscape and Challenges beyond COVID-19)
Show Figures

Figure 1

18 pages, 1652 KiB  
Article
A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
by Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar and Omar Alfandi
Future Internet 2021, 13(4), 90; https://doi.org/10.3390/fi13040090 - 31 Mar 2021
Cited by 27 | Viewed by 3654
Abstract
In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft [...] Read more.
In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the infected transactions in a minimal time. The proposed parallel algorithm is based on the transaction dependency paradigm, with a time complexity O((M+NQ+N^3)/L) (M = total number of transactions under scrutiny, N = number of malicious and affected transactions in the testing list, Q = time for dependency check, and L = number of threads used). The memory complexity of the algorithm is O(N+KL) (N = number of malicious and affected transactions, K = number of transactions in one area handled by one thread, and L = number of threads). Since the damage assessment time is directly proportional to the denial-of-service time, the proposed algorithm provides a minimized execution time. Our algorithm is a novel approach that outperforms other existing algorithms in this domain in terms of both time and memory, working up to four times faster in terms of time and with 120,000 fewer bytes in terms of memory. Full article
(This article belongs to the Special Issue Privacy and Security Landscape and Challenges beyond COVID-19)
Show Figures

Figure 1

Back to TopTop