Security framework for smart cyber infrastructure

Shalaka Satam*, Pratik Satam, Jesus Pacheco, Salim Hariri

*Autor correspondiente de este trabajo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

The rapid deployment of the Internet of Things (IoT) devices have led to the development of innovative information services, unavailable a few years ago. To provide these services, IoT devices connect and communicate using networks like Bluetooth, Wi-Fi, and Ethernet. This full-stack connection of the IoT devices has introduced a grand security challenge. This paper presents an IoT security framework to protect smart infrastructures from cyber attacks. This IoT security framework is applied to Bluetooth protocol and IoT sensors networks. For the Bluetooth protocol, the intrusion detection system (IDS) uses n-grams to extract temporal and spatial features of Bluetooth communication. The Bluetooth IDS has a precision of 99.6% and a recall of 99.6% using classification technique like Ripper algorithm and Decision Tree (C4.5). We also used AdaBoost, support vector machine (SVM), Naive Bayes, and Bagging algorithm for intrusion detection. The Sensor IDS uses discrete wavelet transform (DWT) to extract spatial and temporal features characteristics of the observed signal. Using the detailed coefficients of Biorthogonal DWT, Daubechies DWT, Coiflets DWT, Discrete Meyer DWT, Reverse Biorthogonal DWT, Symlets DWT, we present the results for detecting attacks with One-Class SVM, Local Outlier Factor, and Elliptic Envelope. The attacks used in our evaluation include Denial of Service Attacks, Impersonation Attacks, Random Signal Attacks, and Replay Attacks on temperature sensors. The One-Class SVM performed the best when compared with the results of other machine learning techniques.

Idioma originalInglés
Páginas (desde-hasta)2767-2778
Número de páginas12
PublicaciónCluster Computing
Volumen25
N.º4
DOI
EstadoPublicada - ago 2022

Nota bibliográfica

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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