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Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (28 May 2023) | Viewed by 12663

Special Issue Editor

Dr. Stefan Poslad

E-Mail Website
Guest Editor
Director, IoT2US Lab, Queen Mary University of London, London, UK
Interests: Internet of Things; ubiquitous computing; smart environments; spatial-awareness; pervasive games; security; privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart environments are environments embedded with smart (including artificially intelligent) networked, digital, devices that enhance everyday tasks and services in a seamless way. Smart environments may be physical, cyber (or virtual or digital), or human (social). There are several key indicators and facilitators for the rise in smart environments, e.g., the rise in the deployments of Internet of Things (IoT) and cyber-physical systems in which a greater range of types of digital devices are embedded, networked, and deployed for a range of application domains, including smart health, buildings, transport, agriculture, cities, and so on. Physical and human smart environments are becoming more intertwined, i.e., everyday life is becoming more of a blend of virtual and in-person living as virtual offices, workouts, increasing use of augmented reality, etc., become an everyday reality.

However, smart environments can lead to new or more frequent types of cybersecurity risks, such as many more attacks on cyber surfaces that are becoming inherently more accessible, the rise in silent cyber-risks, and less inbuilt security at design for some (e.g., lower-resource) devices that may also be embedded in more complex systems.

The prevalence of smart environments is accompanied by a growth in privacy risks due to the increasing likelihood that our locations, movements, physical and cyber actions can be tracked more pervasively, opportunistically, or inadvertently via a by-default opt-in by merely being present in smart environments.

This Special Issue seeks to collect the latest research and innovations concerning “Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions”.

Dr. Stefan Poslad
Guest Editor

Manuscript Submission Information

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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. Sensors 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 2600 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

  • smart environment
  • cyber-physical systems
  • ambient intelligence
  • AI
  • cybersecurity
  • privacy

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Published Papers (4 papers)

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Research

17 pages, 6302 KiB  
Article
Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment
by Adil O. Khadidos, Zenah Mahmoud AlKubaisy, Alaa O. Khadidos, Khaled H. Alyoubi, Abdulrhman M. Alshareef and Mahmoud Ragab
Sensors 2023, 23(16), 7207; https://doi.org/10.3390/s23167207 - 16 Aug 2023
Cited by 6 | Viewed by 1396
Abstract
Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to [...] Read more.
Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions)
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14 pages, 902 KiB  
Article
Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks
by Abdulaziz Fatani, Abdelghani Dahou, Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Songfeng Lu, Saad Ali Alfadhli and Shayem Saleh Alresheedi
Sensors 2023, 23(9), 4430; https://doi.org/10.3390/s23094430 - 30 Apr 2023
Cited by 34 | Viewed by 3830
Abstract
Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and [...] Read more.
Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions)
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25 pages, 1103 KiB  
Article
Information Security Threats and Working from Home Culture: Taxonomy, Risk Assessment and Solutions
by Jaidip Kotak, Edan Habler, Oleg Brodt, Asaf Shabtai and Yuval Elovici
Sensors 2023, 23(8), 4018; https://doi.org/10.3390/s23084018 - 15 Apr 2023
Cited by 6 | Viewed by 3434
Abstract
During the COVID-19 pandemic, most organizations were forced to implement a work-from-home policy, and in many cases, employees have not been expected to return to the office on a full-time basis. This sudden shift in the work culture was accompanied by an increase [...] Read more.
During the COVID-19 pandemic, most organizations were forced to implement a work-from-home policy, and in many cases, employees have not been expected to return to the office on a full-time basis. This sudden shift in the work culture was accompanied by an increase in the number of information security-related threats which organizations were unprepared for. The ability to effectively address these threats relies on a comprehensive threat analysis and risk assessment and the creation of relevant asset and threat taxonomies for the new work-from-home culture. In response to this need, we built the required taxonomies and performed a thorough analysis of the threats associated with this new work culture. In this paper, we present our taxonomies and the results of our analysis. We also examine the impact of each threat, indicate when it is expected to occur, describe the various prevention methods available commercially or proposed in academic research, and present specific use cases. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions)
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14 pages, 1750 KiB  
Article
Security Analysis of Cyber-Physical Systems Using Reinforcement Learning
by Mariam Ibrahim and Ruba Elhafiz
Sensors 2023, 23(3), 1634; https://doi.org/10.3390/s23031634 - 2 Feb 2023
Cited by 9 | Viewed by 3114
Abstract
Future engineering systems with new capabilities that far exceed today’s levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study [...] Read more.
Future engineering systems with new capabilities that far exceed today’s levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study of a smart grid by using a reinforcement learning (RL) augmented attack graph to effectively highlight the subsystems’ weaknesses. In particular, the state action reward state action (SARSA) RL technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment. SARSA uses rewards and penalties to identify the worst-case attack scenario; with the most cumulative reward, an attacker may carry out the most harm to the system with the fewest available actions. Results showed successfully the worst-case attack scenario with a total reward of 26.9 and identified the most severely damaged subsystems. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Environments: Current Research Trends and Future Directions)
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