Security Challenges for the Internet of Things and Mobile Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 4706

Special Issue Editors

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: network security; privacy-preserving; cryptography; authentication; artificial intelligence; blockchain
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
Interests: underwater acoustic communication networks; wireless ad hoc networks; cross-layer design; medium access control protocols; network routing protocols; underwater network node localization; underwater sensor networks

Special Issue Information

Dear Colleagues,

In this era of technological innovation, the Internet of Things (IoT) is one of the most popular technologies. As devices and technologies become smarter and more connected, they face increasing security problems and vulnerabilities. As many smart devices are resource-constrained and have limited computing power, they cannot run strong and resource-intensive security protections and may have more vulnerabilities than non-IoT devices. Insecure communication is another serious problem in IoT networks. Most existing security mechanisms were originally designed for desktop computers and are difficult to implement on resource-constrained IoT devices. IoT devices are extremely vulnerable to various forms of attack, including man-in-the-middle attacks, if the device does not use secure encryption and authentication mechanisms. Unfortunately, traditional encryption and authentication mechanisms are not suitable for resource-limited IoT devices. The issue of privacy leakage is one of the serious security problems of IoT. Attackers can easily obtain unencrypted sensitive information, such as location information, bank account details and health records from IoT devices. Malware risk and cyberattacks are two other concerns for IoT security. If attackers find a way to inject malware into IoT systems, they could alter their functionality, collect personal data and launch other attacks. IoT suffers from denial of service attacks, denial of sleep attacks, device spoofing attacks, physical intrusion and other network attacks. Due to the bandwidth, energy and computational constraints in IoT systems, efficient novel methods are required to reach the required security level. The purpose of this Special Issue is to advance this effort by inviting contributions addressing security problems, mitigations and tools in IoT. Topics of interest include but are not limited to the following areas:

  • Lightweight cryptography, key management, authentication and authorization for IoT;
  • Security and privacy enhancing tools for IoT;
  • Lightweight security protocols for IoT;
  • IoT systems and network security;
  • Intrusion and vulnerability anomaly detection in IoT systems;
  • Artificial intelligence (AI)-based security and data protection for IoT;
  • Hardware and firmware security for IoT.

Dr. Xiaofen Wang
Dr. Ruiqin Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things (IoT)
  • security
  • privacy protection
  • authentication
  • man-in-middle attacks
  • malware
  • denial of service attacks
  • denial of sleep attacks
  • device spoofing attacks
  • key management
  • intrusion and vulnerability detection

Published Papers (4 papers)

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Research

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20 pages, 739 KiB  
Article
Finding Taint-Style Vulnerabilities in Lua Application of IoT Firmware with Progressive Static Analysis
Appl. Sci. 2023, 13(17), 9710; https://doi.org/10.3390/app13179710 - 28 Aug 2023
Viewed by 919
Abstract
With the rapid growth of IoT devices, ensuring the security of embedded firmware has become a critical concern. Despite advances in existing vulnerability discovery methods, previous research has been limited to vulnerabilities occurring in binary programs. Although an increasing number of vendors are [...] Read more.
With the rapid growth of IoT devices, ensuring the security of embedded firmware has become a critical concern. Despite advances in existing vulnerability discovery methods, previous research has been limited to vulnerabilities occurring in binary programs. Although an increasing number of vendors are utilizing Lua scripting language in firmware development, no automated method is currently available to discover vulnerabilities in Lua-based programs. To fill this gap, in this paper, we propose FLuaScan, a novel progressive static analysis approach specifically designed to detect taint-style vulnerabilities in Lua applications in IoT firmware. FLuaScan first heuristically locates the code that handles user input, then divides the code into different segments to conduct a progressive taint analysis. Finally, a graph-based search method is applied to identify vulnerable code that satisfies the conditions of taint propagation. To comprehensively compare FLuaScan with state-of-the-art tool Tscancode, we conducted various experiments on a dataset consisting of 13 real-world firmware samples from different vendors. The results demonstrate the superior performance of FLuaScan in terms of accuracy (increased TP rate from 0% to 42.50%), effectiveness (discovered 21 vulnerabilities, of which 7 are unknown), and practicality (acceptable time overhead and visual output to assist in manual analysis). Full article
(This article belongs to the Special Issue Security Challenges for the Internet of Things and Mobile Networks)
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16 pages, 810 KiB  
Article
An Efficient Confidence Interval-Based Dual-Key Fuzzy Vault Scheme for Operator Authentication of Autonomous Unmanned Aerial Vehicles
Appl. Sci. 2023, 13(15), 8894; https://doi.org/10.3390/app13158894 - 02 Aug 2023
Viewed by 611
Abstract
The fuzzy vault is an innovative way to share secret keys, combining traditional cryptography with biometrics and biometric template protection. This method forms the basis for the reliable operation of unmanned aerial vehicles (UAVs) through anonymizing drone operators and safely using their data [...] Read more.
The fuzzy vault is an innovative way to share secret keys, combining traditional cryptography with biometrics and biometric template protection. This method forms the basis for the reliable operation of unmanned aerial vehicles (UAVs) through anonymizing drone operators and safely using their data and onboard information. However, due to the inherent instability of biometrics, traditional fuzzy vault schemes face challenges, such as reduced recognition rates with increased chaff points, impractical runtimes due to high-order polynomial reconstruction, and susceptibility to correlation attacks. This paper proposes an efficient fuzzy vault scheme to address these challenges. We generate two secret keys based on biometrics: the first key is produced from the operator’s unique features like the face and iris, using a confidence interval; the second key, used to construct a polynomial, is based on what the operator remembers. These dual-key fuzzy vaults enable the stable generation of genuine points during encoding, easy extraction during decoding, and effective operator authentication while maintaining anonymity. Our experimental results demonstrate improved security and secret acquisition accuracy using the AR face database. These results are achieved regardless of increased false vaults, enabling real-time polynomial reconstruction and resilience against correlation attacks. Importantly, our enhanced fuzzy vault scheme allows the application of this secure, real-time authentication process, safeguarding the anonymity of drone operators. Full article
(This article belongs to the Special Issue Security Challenges for the Internet of Things and Mobile Networks)
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24 pages, 504 KiB  
Article
VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
Appl. Sci. 2023, 13(9), 5733; https://doi.org/10.3390/app13095733 - 06 May 2023
Cited by 1 | Viewed by 1608
Abstract
Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it [...] Read more.
Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation & management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM’s security algorithm effectiveness, accuracy, and ability to resist several attacks. Full article
(This article belongs to the Special Issue Security Challenges for the Internet of Things and Mobile Networks)
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Review

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23 pages, 5701 KiB  
Review
Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
Appl. Sci. 2023, 13(21), 11908; https://doi.org/10.3390/app132111908 - 31 Oct 2023
Cited by 1 | Viewed by 1171
Abstract
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day [...] Read more.
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day attacks, polymorphic malware, increasing signature base, and detection speed. To achieve rapid detection, automated system-level malware detection using machine learning approaches, leveraging the power of artificial intelligence to identify and mitigate malware attacks, has emerged as a promising solution. This comprehensive review aims to provides a detailed analysis of the status quo in malware detection by exploring the fundamentals of machine learning techniques for malware detection. The review is largely based on the PRISMA approach for article search methods and selection from four databases. Keywords were identified together with inclusion and exclusion criteria. The review seeks feature extraction and selection methods that enhance the accuracy and precision of detection algorithms. Evaluation metrics and common datasets were used to assess the performance of the system-level malware detection techniques. A comparative analysis of different machine learning approaches, emphasizing their strengths, weaknesses, and performance in detecting system-level malware is presented together with the limitations of the detection techniques. The paper concludes with future research opportunities, particularly in applying artificial intelligence, and provides a resource for researchers and cybersecurity professionals seeking to understand and advance automated system-level malware detection using machine learning. Full article
(This article belongs to the Special Issue Security Challenges for the Internet of Things and Mobile Networks)
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