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Sensor Networks Security and Applications
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Sensor Networks Security and Applications

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 11715

Special Issue Editors

Dr. José L. Hernández Ramos

E-Mail Website
Guest Editor
European Commission, Joint Research Centre, Ispra 21027, Italy
Interests: cybersecurity; privacy; Internet of Things; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios Kambourakis

E-Mail Website
Guest Editor
Department of Information and Communication Systems Engineering, University of the Aegean, 83100 Samos, Greece
Interests: mobile and wireless networks security and privacy; VoIP security; IoT security and privacy; DNS security; intrusion detection systems; security education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erol Gelenbe

E-Mail Website
Guest Editor
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
Interests: energy optimization; energy packet networks; networked systems; physical and biological networks; probability models; natural computation
Special Issues, Collections and Topics in MDPI journals
Dr. Gianmarco Baldini

E-Mail Website
Guest Editor
Joint Research Centre, European Commission, 21027 Ispra, Italy
Interests: cybersecurity; machine learning; deep learning; signal processing; Internet of things; wireless communications; sensors

Special Issue Information

Dear Colleagues,

The current trend towards an interconnected society is being driven by an unprecedented technological convergence, which enables the development of new data-driven applications. For the realization of these applications, sensor networks represent a key component for the collection and exchange of large volumes of data through heterogeneous network technologies in the context of the well-known Internet of Things (IoT) paradigm. This data can be shared and correlated through artificial intelligence techniques to derive information and knowledge that can be used to support the deployment of applications in different domains, like eHealth, Industry 4.0 or smart mobility, and many other applictions.

However, the massive deployment of sensor networks in our daily lives entails an increase of cybersecurity risks that can ultimately affect the safety of citizens. This is especially relevant in certain scenarios, such as body area networks, which are used to exchange medical data about patients. Additionally, while in recent years there has been a growing interest in the development of secure sensor networks, there are still a number of challenges that need to be considered to leverage the benefits of new data-driven applications. In particular, widely deployed cryptographic mechanisms and security protocols may be impractical in this context due to the limitations of underlying network technologies, or the constrained resources of interconnected sensors and actuators. Furthermore, the potentially huge number of these devices requires automated and efficient mechanisms to foster secure deployment. Moreover, security solutions for sensor networks need to manage the changing and dynamic environment (e.g., due to software updates or loss of network connectivity) where such devices could be deployed. These changes may be also due to new identified vulnerabilities and security attacks that need to be detected and mitigated automatically to adapt the network behavior accordingly. Additionally, the protection of exchanged data needs to be addressed from a technological and legal perspective, taking into account current regulations on security and data protection. In the coming years, it is expected that the development of security solutions for sensor networks can take advantage of emerging technologies, such as the use of blockchain, or the development of new distributed artificial intelligence techniques, such as federated learning.

Thus, this special issue seeks original contributions focused on improving the security of sensor networks for the sake of enabling the development of trustworthy data-driven services and applications.

Dr. José L. Hernández Ramos
Dr. Georgios Kambourakis
Prof. Dr. Erol Gelenbe
Dr. Gianmarco Baldini
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.

Keywords

  • Identity management and access control in sensor networks
  • Trust management in sensor networks
  • Lightweight cryptography for sensor networks
  • Risk management in sensors networks
  • Intrusion detection systems for sensor networks
  • Privacy-preserving data analytics
  • Blockchain-based approaches to improve sensor networks security
  • Network wide security and vulnerability assessment
  • Application of software-defined networks (SDN) and network functions virtualization (NFV) in sensor networks
  • Evolution of sensor networks toward 5G-enabled IoT systems
  • Innovative use cases and scenarios based on sensor networks
  • Data-centric security for sensor networks
  • Application of machine learning techniques for intelligent security in sensor networks
  • Federated learning in sensor networks
  • Secure orchestration of sensor networks
  • Security in low-power wide-area networks (LPWAN)
  • Efficient and scalable security for sensor networks
  • Standardization activities for secure sensor networks
  • Enforcement of data protection regulations based on secure sensor networks

Published Papers (3 papers)

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Research

20 pages, 3759 KiB  
Article
Malicious Network Behavior Detection Using Fusion of Packet Captures Files and Business Feature Data
by Mingshu He, Xiaojuan Wang, Lei Jin, Bingying Dai, Kaiwenlv Kacuila and Xiaosu Xue
Sensors 2021, 21(17), 5942; https://doi.org/10.3390/s21175942 - 03 Sep 2021
Cited by 2 | Viewed by 2493
Abstract
Information and communication technologies have essential impacts on people’s life. The real time convenience of the internet greatly facilitates the information transmission and knowledge exchange of users. However, network intruders utilize some communication holes to complete malicious attacks. Some traditional machine learning (ML) [...] Read more.
Information and communication technologies have essential impacts on people’s life. The real time convenience of the internet greatly facilitates the information transmission and knowledge exchange of users. However, network intruders utilize some communication holes to complete malicious attacks. Some traditional machine learning (ML) methods based on business features and deep learning (DL) methods extracting features automatically are used to identify these malicious behaviors. However, these approaches tend to use only one type of data source, which can result in the loss of some features that can not be mined in the data. In order to address this problem and to improve the precision of malicious behavior detection, this paper proposed a one-dimensional (1D) convolution-based fusion model of packet capture files and business feature data for malicious network behavior detection. Fusion models improve the malicious behavior detection results compared with single ones in some available network traffic and Internet of things (IOT) datasets. The experiments also indicate that early data fusion, feature fusion and decision fusion are all effective in the model. Moreover, this paper also discusses the adaptability of one-dimensional convolution and two-dimensional (2D) convolution to network traffic data. Full article
(This article belongs to the Special Issue Sensor Networks Security and Applications)
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37 pages, 5380 KiB  
Article
Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset
by Zhipeng Liu, Niraj Thapa, Addison Shaver, Kaushik Roy, Madhuri Siddula, Xiaohong Yuan and Anna Yu
Sensors 2021, 21(14), 4834; https://doi.org/10.3390/s21144834 - 15 Jul 2021
Cited by 26 | Viewed by 4830
Abstract
As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many [...] Read more.
As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks. Full article
(This article belongs to the Special Issue Sensor Networks Security and Applications)
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25 pages, 2741 KiB  
Article
Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime
by Rami Ahmad, Raniyah Wazirali, Qusay Bsoul, Tarik Abu-Ain and Waleed Abu-Ain
Sensors 2021, 21(14), 4821; https://doi.org/10.3390/s21144821 - 15 Jul 2021
Cited by 25 | Viewed by 2966
Abstract
Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable [...] Read more.
Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios. Full article
(This article belongs to the Special Issue Sensor Networks Security and Applications)
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