Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Spadaccino, Pietro; Cuomo, Francesca

doi:10.52953/wnvi5792

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…Several methods and frameworks alleviate various layer attacks using ML and DL techniques [47][48][49]. Many kinds of literature have been published to classify anomalies using ML methods in the IoT infrastructure [50][51][52][53]. Depending upon the methods, we surveyed the literature to learn the recent algorithms used.…”

Section: Intrusion Detection System In Iotmentioning

confidence: 99%

Anomaly-based Intrusion Detection System for IoT Application

Bhavsar

Roy

Kelly

et al. 2023

Preprint

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Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, and many more. Those applications are increasingly popular because they address real-time problems. In contrast, the use of transmission and communication protocols have raised serious security concerns for IoT devices, and traditional methods such as signature and rule-based methods are inefficient for securing these devices. Hence, identifying network traffic behavior and mitigating cyber attacks are important in IoT to provide guaranteed network security. Therefore, we develop an Intrusion Detection System (IDS) based on a deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) to detect network anomalies. The PCC-CNN model combines the important features obtained from the linear-based extractions followed by the Convolutional Neural Network. It performs a Binary classification for Anomaly detection and also a Multiclass classification for various types of attacks. The model is evaluated on three publicly available datasets: NSL-KDD, CICIDS-2017, and IOTID20. We first train and test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification and Regression Tree,\& Support Vector Machine) PCC-based Machine Learning models to evaluate the model performance. We achieve the best similar accuracy from the KNN and CART model of 98\%, 99\%, and 98\%, respectively, on the three datasets. On the other hand, we achieve a promising performance with a better detection accuracy of 99.89\% and with a low misclassification rate of 0.001 with our proposed PCC-CNN model. The integrated model is promising, with a misclassification rate (or False alarm rate) of 0.02, 0.02, and 0.00 with Binary and Multiclass intrusion detection classifiers. Finally, we compare and discuss our PCC-CNN model in comparison to five traditional PCC-ML models. Our proposed Deep Learning (DL)-based IDS outperforms traditional methods.

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Section: Intrusion Detection System In Iotmentioning

confidence: 99%

Anomaly-based Intrusion Detection System for IoT Application

Bhavsar

Roy

Kelly

et al. 2023

Preprint

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“…From the development situation in recent years, the development level of preschool education has been significantly improved, and the demand for preschool education teachers is also rising [15]. It is undeniable that for modern society, higher education has obvious characteristics of popularization, but most kindergarten teachers are provided by the preschool education major of secondary vocational schools [16].…”

Section: Introductionmentioning

confidence: 99%

Application of Edge Computing and Data Mining Processing System in Preschool Education Courses

2022

Mobile Information Systems

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With the deepening of reform and opening up, my country’s preschool education has entered a vigorous development. At the same time, higher requirements are put forward for the field of preschool education. Preschool education is an important part of education, and preschool education is the foundation of basic education. However, the informatization construction in the field of preschool education in my country has just started. In order to implement the planning requirements of the Ministry of Education on strengthening the informatization construction of preschool education, build a digital platform, improve children’s quality, promote the harmonious development of children’s body and mind, improve professional ethics and the professional level of preschool education practitioners with the help of modern new media, and meet the professional development and sense of achievement, acquisition, and satisfaction of preschool education practitioners. Taking multiple measures to ensure the diversified needs of preschool education practitioners is the important and only means for the stable, comprehensive, and sustainable development of preschool education. The data visualization platform of IOT architecture designed in this paper for edge computing ensures the application of massive data in preschool education courses, ensures the stability of system data transmission, and realizes the visualization of IOT terminal data. Therefore, the introduction of data mining technology in this paper can improve the utilization of resources, provide support for teachers, students, and ordinary users, and enhance the versatility of the preschool education resource platform.

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IoT Cyber Attacks Detection - Survey

Zubaidi

2024

2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

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Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Cited by 5 publications

References 48 publications

Anomaly-based Intrusion Detection System for IoT Application

Anomaly-based Intrusion Detection System for IoT Application

Application of Edge Computing and Data Mining Processing System in Preschool Education Courses

IoT Cyber Attacks Detection - Survey

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