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Sensor Data Privacy and Intrusion Detection for IoT Networks
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Sensor Data Privacy and Intrusion Detection for IoT Networks

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

Deadline for manuscript submissions: closed (25 April 2025) | Viewed by 2586

Special Issue Editor

Dr. Morshed Chowdhury

E-Mail Website
Guest Editor
School of Info Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: security of the Internet of Things; wireless network security; health data analytics; documentation security

Special Issue Information

Dear Colleagues,

The Internet of Things has begun to evolve its applications in various sectors of human life through smart and portable devices and wireless communication. Sensor data collection, aggregation, transmission, and analysis in IoT networks should be carried out without any data disclosure. Fortunately, highly secure and privacy-sensitive data in IoT networks can be protected with intelligent privacy-preserving techniques and trustable data processing mechanisms. Of course, data owners and service providers would always like to utilize privacy-aware data trading mechanisms. Even so, security risks in IoT networks are increasing due to the limited security protection of devices and the decentralized nature of routing and communication. Furthermore, the limited resource capability of IoT devices requires special infrastructure to run Intrusion Detection Systems like fog nodes or edge nodes, and the design of an intelligent and energy-efficient IDS is highly challenging for IoT networks where 0-day attack vulnerabilities are becoming more frequent. Thus, offering intelligent and ubiquitous services and data to customers under various security threats via IoT networks is the primary objective of this Special Issue.

This Special Issue focuses on the recent developments, technologies, applications, and challenges presented in original research and review papers related to sensor data privacy and intrusion detection systems for IoT networks.

Dr. Morshed Chowdhury
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. 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

  • privacy-aware sensor data analysis
  • privacy-aware sensor data trading
  • sensor data confidentiality
  • O-Day attack
  • intelligent intrusion detection systems
  • secure routing protocol for IoT networks
  • security architecture framework for sensor data privacy
  • access control and authentication
  • blockchain technologies
  • continuous learning model for IDS
  • edge computing for sensor data privacy and IDS
  • fog node for IDS

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Published Papers (2 papers)

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Research

26 pages, 3796 KiB  
Article
An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks
by Taiwo Blessing Ogunseyi and Gogulakrishan Thiyagarajan
Sensors 2025, 25(7), 2288; https://doi.org/10.3390/s25072288 - 4 Apr 2025
Viewed by 570
Abstract
As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed [...] Read more.
As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed threats due to the presence of redundant and irrelevant information for the IDSs. Furthermore, recent IDSs that utilize artificial intelligence are often presented as black boxes, offering no explanation of their internal operations. In this study, we develop a solution to the identified challenges by presenting a deep learning-based model that adapts to new attacks by selecting only the relevant information as inputs and providing transparent internal operations for easy understanding and adoption by cybersecurity personnel. Specifically, we employ a hybrid approach using statistical methods and a metaheuristic algorithm for feature selection to identify the most relevant features and limit the overall feature set while building an LSTM-based model for intrusion detection. To this end, we utilize two publicly available datasets, NF-BoT-IoT-v2 and IoTID20, for training and testing. The results demonstrate an accuracy of 98.42% and 89.54% for the NF-BoT-IoT-v2 and IoTID20 datasets, respectively. The performance of the proposed model is compared with that of other machine learning models and existing state-of-the-art models, demonstrating superior accuracy. To explain the proposed model’s predictions and increase trust in its outcomes, we applied two explainable artificial intelligence (XAI) tools: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), providing valuable insights into the model’s behavior. Full article
(This article belongs to the Special Issue Sensor Data Privacy and Intrusion Detection for IoT Networks)
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15 pages, 941 KiB  
Article
Embedding Tree-Based Intrusion Detection System in Smart Thermostats for Enhanced IoT Security
by Abbas Javed, Muhammad Naeem Awais, Ayyaz-ul-Haq Qureshi, Muhammad Jawad, Jehangir Arshad and Hadi Larijani
Sensors 2024, 24(22), 7320; https://doi.org/10.3390/s24227320 - 16 Nov 2024
Cited by 1 | Viewed by 1219
Abstract
IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While [...] Read more.
IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While machine learning-based IDS have typically been deployed at the edge (gateways) or in the cloud, in the absence of gateways, the IDS must be embedded within the sensor nodes themselves. Available datasets mainly contain features extracted from network traffic at the edge (e.g., Raspberry Pi/computer) or cloud servers. We developed a unique dataset, named as Intrusion Detection in the Smart Homes (IDSH) dataset, which is based on features retrievable from microcontroller-based IoT devices. In this work, a Tree-based IDS is embedded into a smart thermostat for real-time intrusion detection. The results demonstrated that the IDS achieved an accuracy of 98.71% for binary classification with an inference time of 276 microseconds, and an accuracy of 97.51% for multi-classification with an inference time of 273 microseconds. Real-time testing showed that the smart thermostat is capable of detecting DoS and MITM attacks without relying on a gateway or cloud. Full article
(This article belongs to the Special Issue Sensor Data Privacy and Intrusion Detection for IoT Networks)
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