Internet of Things: A Survey of Technologies and Security Risks in Smart Home and City Environments

Bastos, Daniel; Shackleton, Mark; El-Moussa, Fadi Ali

doi:10.1049/cp.2018.0030

Cited by 66 publications

(33 citation statements)

References 2 publications

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“…First, cryptographic keys or certificates cannot be efficiently distributed due to the limited computing power leading to weak authentication [1]. Second, default keys or credentials can be brute-forced by high-computing-power attackers, extracted from device firmware or mobile apps, or intercepted at login [2]. Third, the ID number such as MAC address in the header needs extra spectral or power resources, which is limited in IoT applications, to be transmitted [3].…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising Au-toEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

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Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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“…While IoT has many potential benefits across domains, from security perspective, it also provides a lucrative motivation to the attackers [6]. These motivations are graphically presented in Fig.2 [6].It may be assumed that an attacker may consider these motivations as an opportunity to exploit the same for malicious intent [6]. The goals of the attackers may include [13]: DDoS attack on internet, breach of privacy, extortion from the users, forging of critical data, illegal data mining, or coordinated attack like cyber warfare.…”

Section: Vulnerabilities and Their Expolits In Iotmentioning

confidence: 99%

“…The deployment of IoT in smart environment stems from objectives of a typical smart city required for e-governance. These may include [6]:a) Optimization of Public services like traffic flow, parking, garbage management, city-lighting, surveillance of hospitals, schools and other public areas, b) Optimization of resources like water supply management, electricity management, allocation of civic staff etc; c) Traffic Management for reduction in congestion , d) Monitoring of City Environment viz. Air Quality index monitoring, pollution monitoring etc; e) Better and Transparent Government viz.…”

Section: Introductionmentioning

confidence: 99%

“…The IoT devices generally connect to the network by wireless access technologies [6]. With the growth of 4G and 5G access technologies across the globe more and more applications are being added for seamless control of the user environment for further augmenting the user experience of service [7] thereby empowering IoT deployments.…”

Section: Introductionmentioning

confidence: 99%

“…With the growth of 4G and 5G access technologies across the globe more and more applications are being added for seamless control of the user environment for further augmenting the user experience of service [7] thereby empowering IoT deployments. With rapid research being undertaken across the globe for IoT devices, Smart parking, Smart Traffic controls and smart watches are acting as pioneers in smart city environments [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Internet of Things in Smart City Environments

Gupta¹,

Vanjale²

2019

IJRTE

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Abstract: With the advent of smart devices, a huge paradigm shift is observed in the way the users define service quality. Further, these devices or Internet of Things (IoT) devices as they are generally addressed, have acted as catalyst for comfort and connectivity and are building blocks of Smart City environment. With limited thought related to security is involved during the deployment of such devices, they offer a dangerous environment of opportunity to the attackers from the internet; which not only jeopardize network security, bus also the privacy of the users. Hence, it is of utmost importance to address the security concerns in smart city environment. This paper attempts to study the current IoT technologies deployed in a smart-city environment along with its vulnerabilities and possible solutions to improve IoT security. An approach is made to study the various vulnerabilities available with the IoT devices deployed in the smart city setup, various motivation of an attacker and the analyse some of the recent attacks witnessed by IoT devices. A few possible solutions for mitigation are suggested in this paper. The findings of the paper can be implemented in any network of IoT devices.

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A Study of DDoS Reflection Attack on Internet of Things in IPv4/IPv6 Networks

Šimon

Huraj

2019

Advances in Intelligent Systems and Computing

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Internet of Things: A Survey of Technologies and Security Risks in Smart Home and City Environments

Cited by 66 publications

References 2 publications

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Internet of Things in Smart City Environments

A Study of DDoS Reflection Attack on Internet of Things in IPv4/IPv6 Networks

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