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Threat Identification and Defence for Internet-of-Things 2021-2022
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Threat Identification and Defence for Internet-of-Things 2021-2022

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 8073

Special Issue Editors

Dr. Weizhi Meng

E-Mail Website
Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: cyber security; intrusion detection; mobile security and authentication; HCI security; malware analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaobo Ma

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: cybersecurity; internet measurement; traffic analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is the network of physical devices and various kinds of embedded software, which enable different Internet-connected objects to exchange data. However, the Internet-enabled devices also bring many new challenges. For example, the fundamental security weakness of IoT is that it increases the number of devices behind a network firewall. In addition, many companies may not update their devices very often, which means that an IoT device that was safe at first will become unsafe if hackers discover new threats and vulnerabilities. As a result, how to protect IoT from various threats is a challenging task.

This Special Issue focuses on all IoT security issues, especially threat detection and defense, and aims to publish recent research studies for IoT development that discuss novel ways in securing IoT security, privacy and trust.

In particular, the topics of interest include, but are not limited to:

  • Secure network architecture for IoT
  • Trust management of IoT
  • Secure data storage and segregation
  • Secure cloud storage and computation for IoT
  • Availability, recovery and auditing for IoT
  • Secure and energy efficient management for IoT
  • IoT cyber crime
  • Denial-of-service attacks for IoT
  • IoT security and privacy- IoT forensic techniques
  • Usable security and privacy for IoT
  • Intrusion detection and prevention for IoT
  • Cyber intelligence techniques for IoT

Dr. Weizhi Meng
Dr. Xiaobo Ma
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. 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.

Published Papers (4 papers)

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Research

10 pages, 2987 KiB  
Article
Automated Android Malware Detection Using User Feedback
by João Duque, Goncalo Mendes, Luís Nunes, Ana de Almeida and Carlos Serrão
Sensors 2022, 22(17), 6561; https://doi.org/10.3390/s22176561 - 31 Aug 2022
Cited by 1 | Viewed by 1455
Abstract
The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking [...] Read more.
The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their forms of automated malware detection in place to deter malware developers and keep their app store (and reputation) trustworthy. Nevertheless, a non-negligible number of apps can bypass these detectors and remain available in the marketplace for any user to download and install on their device. Current malware detection strategies rely on using static or dynamic app extracted features (or a combination of both) to scale the detection and cover the growing number of apps submitted to the marketplace. In this paper, the main focus is on the apps that bypass the malware detectors and stay in the marketplace long enough to receive user feedback. This paper uses real-world data provided by an app store. The quantitative ratings and potential alert flags assigned to the apps by the users were used as features to train machine learning classifiers that successfully classify malware that evaded previous detection attempts. These results present reasonable accuracy and thus work to help to maintain a user-safe environment. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things 2021-2022)
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16 pages, 634 KiB  
Article
Fuzzy-Based Privacy-Preserving Scheme of Low Consumption and High Effectiveness for IoTs: A Repeated Game Model
by Laicheng Cao and Min Zhu
Sensors 2022, 22(15), 5674; https://doi.org/10.3390/s22155674 - 29 Jul 2022
Cited by 1 | Viewed by 1118
Abstract
In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving [...] Read more.
In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things 2021-2022)
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16 pages, 286 KiB  
Article
Improving Network-Based Anomaly Detection in Smart Home Environment
by Xiaonan Li, Hossein Ghodosi, Chao Chen, Mangalam Sankupellay and Ickjai Lee
Sensors 2022, 22(15), 5626; https://doi.org/10.3390/s22155626 - 27 Jul 2022
Cited by 3 | Viewed by 1753
Abstract
The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH [...] Read more.
The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things 2021-2022)
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19 pages, 56462 KiB  
Article
IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework
by Swarnabha Roy, Tony Vo, Steven Hernandez, Austin Lehrmann, Asad Ali and Stavros Kalafatis
Sensors 2022, 22(15), 5569; https://doi.org/10.3390/s22155569 - 26 Jul 2022
Cited by 8 | Viewed by 2674
Abstract
There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a [...] Read more.
There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV–UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things 2021-2022)
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