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Machine Learning for Wireless Sensor Network and IoT Security

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 8959

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Dr. Achyut Shankar

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Department of Cyber Security, University of Warwick, Coventry CV4 7AL, UK
Interests: wireless sensor networks; Internet of Things; blockchain technology; cloud computing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

Today, there is an exponentially increasing amount of data from multiple wireless sensors, such as cameras, webcams, or other optical or radar sensors. Proper ways of mining and using these data could make great contributions to the development of civil and military technologies. For example, optical and radar images can be employed to detect and recognize the interested targets in a large scene to help intelligence interpretation and battlefield surveillance. Furthermore, data from the two types of wireless sensors can be properly fused to find more latent information. This Special Collection welcomes original research and review articles with a focus on applying advanced machine learning approaches in data processing from multiple wireless sensor security. The issue aims to provide novel guidance for machine learning researchers and broaden the perspectives of machine learning and IoT-sensor-related researchers.

Research areas may include (but are not limited to) the following topics:

Machine learning in optical image processing of wireless sensor networks;
Machine learning in video processing of wireless sensor networks;
Machine learning in radar signal/image processing of wireless sensor networks;
Machine learning in the Internet of Things (IoT);
Machine learning in multi-sensor data fusion of wireless sensor networks;
Machine learning in the cooperative working of multiple wireless sensors.

Dr. Achyut Shankar
Guest Editor

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Keywords

wireless sensor network
Internet of Things
machine learning
security

Published Papers (5 papers)

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Research

29 pages, 6232 KiB

Open AccessArticle

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

by Anandaraj Mahalingam, Ganeshkumar Perumal, Gopalakrishnan Subburayalu, Mubarak Albathan, Abdullah Altameem, Riyad Saleh Almakki, Ayyaz Hussain and Qaisar Abbas

Sensors 2023, 23(19), 8044; https://doi.org/10.3390/s23198044 - 23 Sep 2023

Cited by 1 | Viewed by 1089

Abstract

The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems. Full article

(This article belongs to the Special Issue Machine Learning for Wireless Sensor Network and IoT Security)

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19 pages, 4443 KiB

Open AccessArticle

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

by Umesh Kumar Lilhore, Poongodi Manoharan, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah, Surjeet Dalal, Ashish Sharma and Kaamran Raahemifar

Sensors 2023, 23(18), 7856; https://doi.org/10.3390/s23187856 - 13 Sep 2023

Cited by 9 | Viewed by 1361

Abstract

Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL. Full article

(This article belongs to the Special Issue Machine Learning for Wireless Sensor Network and IoT Security)

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17 pages, 4035 KiB

Open AccessArticle

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices

by Anil Kumar Budati, Shayla Islam, Mohammad Kamrul Hasan, Nurhizam Safie, Nurhidayah Bahar and Taher M. Ghazal

Sensors 2023, 23(11), 5072; https://doi.org/10.3390/s23115072 - 25 May 2023

Cited by 5 | Viewed by 1544

Abstract

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput. Full article

(This article belongs to the Special Issue Machine Learning for Wireless Sensor Network and IoT Security)

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17 pages, 3855 KiB

Open AccessArticle

Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks

by Mohammed Aljebreen, Manal Abdullah Alohali, Muhammad Kashif Saeed, Heba Mohsen, Mesfer Al Duhayyim, Amgad Atta Abdelmageed, Suhanda Drar and Sitelbanat Abdelbagi

Sensors 2023, 23(8), 4073; https://doi.org/10.3390/s23084073 - 18 Apr 2023

Cited by 4 | Viewed by 1359

Abstract

An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively. Full article

(This article belongs to the Special Issue Machine Learning for Wireless Sensor Network and IoT Security)

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23 pages, 2805 KiB

Open AccessArticle

Intelligent Transportation Using Wireless Sensor Networks Blockchain and License Plate Recognition

by Fares Alharbi, Mohammed Zakariah, Reem Alshahrani, Ashwag Albakri, Wattana Viriyasitavat and Abdulrahman Abdullah Alghamdi

Sensors 2023, 23(5), 2670; https://doi.org/10.3390/s23052670 - 28 Feb 2023

Cited by 5 | Viewed by 2180

Abstract

License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user’s license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys. Full article

(This article belongs to the Special Issue Machine Learning for Wireless Sensor Network and IoT Security)

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