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Security in Internet of Things (IoT): Challenges, Solutions and Future Directions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 5587

Special Issue Editor

Dr. Baraq Ghaleb

E-Mail Website
Guest Editor
School of Computing, Engineering & the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: Internet of Things; IoT security; cyber security; routing protocols and cross-layer design; blockchain; machine learning; wireless body area networks; and e-health and wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, the Internet-of-Things (IoT) has made the leap from conceptual to actual, paving the way for a wide range of applications and digital services, such as smart homes and cities, smart grids, wearables, connected healthcare, and environmental monitoring, resulting in unprecedented levels of connectivity across the world. IoT networks comprise a slew of embedded sensor devices with limited processing, storage, and power, resources interlinked by various interconnects that often exhibit considerable unreliability, low data rates, and instability. The limited resources of IoT devices coupled with the constraints imposed by their interconnects have raised numerous security challenges that need to be tackled to pave the way for wider adoption of IoT. In addition, the manufacturers of IoT devices usually overlook security, rendering their released products vulnerable to attacks. These security issues are further complicated by the heterogeneity of IoT networks and devices, thus increasing the difficulty of deploying all-inclusive security solutions. Accordingly,  it is crucial to devise solutions that address such security gaps, a field that is attracting overwhelming attention from researchers. Hence, this Special Issue aims to bring together novel contributions and recent advances from both academia and industry in the field of IoT security. 

Topics of interest include, but are not limited to:

  • Secure IoT communication protocols and technologies; 
  • Intrusion detection systems for IoT; 
  • Threat modeling and risk assessment in IoT; 
  • Malware detection in IoT; 
  • Privacy-preserving techniques for IoT;
  • Novel attacks in IoT and related countermeasures;
  • Lightweight and Homomorphic security protocols for IoT; 
  • ML-based intrusion detection in IoT;
  • Security protocols in 5G/6G IoT networks; 
  • Secure protocols for IoT standards (IPv6, 6LoWPAN, RPL, 6TiSCH, etc.).

Dr. Baraq Ghaleb
Guest Editor

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. Applied Sciences 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 2400 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

  • secure IoT communication protocols and technologies
  • intrusion detection systems for IoT
  • threat modeling and risk assessment in IoT
  • malware detection in IoT
  • privacy-preserving techniques for IoT
  • novel attacks in IoT and related countermeasures
  • lightweight and Homomorphic security protocols for IoT
  • ML-based intrusion detection in IoT
  • security protocols in 5G/6G IoT networks
  • secure protocols for IoT standards (IPv6, 6LoWPAN, RPL, 6TiSCH, etc.)

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Published Papers (4 papers)

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Research

16 pages, 693 KiB  
Article
Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things
by Yunlong Li, Rongguang Zhang, Pengcheng Zhao and Yunkai Wei
Appl. Sci. 2024, 14(13), 5555; https://doi.org/10.3390/app14135555 - 26 Jun 2024
Cited by 1 | Viewed by 1252
Abstract
Recent years have witnessed the fast development of the Financial Internet of Things (FIoT), which integrates the Internet of Things (IoT) into financial activities. At the same time, the FIoT is facing an increasing number of stealthy network attacks. Long short-term memory (LSTM) [...] Read more.
Recent years have witnessed the fast development of the Financial Internet of Things (FIoT), which integrates the Internet of Things (IoT) into financial activities. At the same time, the FIoT is facing an increasing number of stealthy network attacks. Long short-term memory (LSTM) can be used as an anomaly-detecting method to perceive such attacks since it specializes in discovering anomaly behaviors through the time correlation in FIoT traffic. However, current LSTM-based anomaly detection schemes have not considered the specific correlations among the features of the whole traffic. In addition, current schemes are usually trained based on local traffic with rare cooperation among different detecting nodes, leading to the result that current schemes usually suffer from insufficient adaptability and low coordination. In this paper, we propose a feature-attended federated LSTM (FAF-LSTM) for FIoT to address the above issues. FAF-LSTM combines feature-attended LSTM and federated learning to make full use of the deep correlation in data and enhance the accuracy of the trained model via cooperation among different detecting nodes. In FAF-LSTM, the features are grouped so that the model can learn the time–spatial correlation inner the flows of each group as well as their impact on the output. Meanwhile, the parameter aggregation is optimized based on feature correlation analysis. Simulations are conducted to verify the effect of FAF-LSTM. The results show that FAF-LSTM has good performance in anomaly detection. Compared with independently trained LSTM and traditional federated learning-based LSTM, FAF-LSTM can improve the detection accuracy by up to 39.22% and 334.36%, respectively. Full article
(This article belongs to the Special Issue Security in Internet of Things (IoT): Challenges, Solutions and Future Directions)
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15 pages, 1505 KiB  
Article
A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments
by Sarah A. Algethami and Sultan S. Alshamrani
Appl. Sci. 2024, 14(11), 4729; https://doi.org/10.3390/app14114729 - 30 May 2024
Cited by 2 | Viewed by 1423
Abstract
The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, [...] Read more.
The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives. Full article
(This article belongs to the Special Issue Security in Internet of Things (IoT): Challenges, Solutions and Future Directions)
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20 pages, 8126 KiB  
Article
Securing Edge Devices: Malware Classification with Dual-Attention Deep Network
by Gasim Alandjani
Appl. Sci. 2024, 14(11), 4645; https://doi.org/10.3390/app14114645 - 28 May 2024
Viewed by 805
Abstract
Detecting malware is a crucial defense mechanism against potential cyber-attacks. However, current methods illustrate significant limitations in achieving high performance while maintaining faster inference on edge devices. This study proposes a novel deep network with dual-attention feature refinement on a two-branch deep network [...] Read more.
Detecting malware is a crucial defense mechanism against potential cyber-attacks. However, current methods illustrate significant limitations in achieving high performance while maintaining faster inference on edge devices. This study proposes a novel deep network with dual-attention feature refinement on a two-branch deep network to learn real-time malware detection on edge platforms. The proposed method introduces lightweight spatial-asymmetric attention for refining the extracted features of its backbone and multi-head attention to correlate learned features from the network branches. The experimental results show that the proposed method can significantly outperform existing methods in quantitative evaluation. In addition, this study also illustrates the practicability of a lightweight deep network on edge devices by optimizing and deploying the model directly on the actual edge hardware. The proposed optimization strategy achieves a frame rate of over 545 per second on low-power edge devices. Full article
(This article belongs to the Special Issue Security in Internet of Things (IoT): Challenges, Solutions and Future Directions)
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29 pages, 2188 KiB  
Article
A Lightweight Mitigation Approach against a New Inundation Attack in RPL-Based IoT Networks
by Mehdi Rouissat, Mohammed Belkheir, Ibrahim S. Alsukayti and Allel Mokaddem
Appl. Sci. 2023, 13(18), 10366; https://doi.org/10.3390/app131810366 - 16 Sep 2023
Cited by 6 | Viewed by 1388
Abstract
Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to [...] Read more.
Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels. Full article
(This article belongs to the Special Issue Security in Internet of Things (IoT): Challenges, Solutions and Future Directions)
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