Landscape of IoT security

Schiller, Eryk; Aidoo, Andy; Fuhrer, Jara; Stahl, Jonathan; Ziörjen, Michael; Stiller, Burkhard

doi:10.1016/j.cosrev.2022.100467

Cited by 114 publications

(59 citation statements)

References 45 publications

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“…These are the most common and destructive malicious objects. Device theft, device manipulation, identity theft, and eavesdropping [25] are all serious threats to IoT networks. When the IoT is compromised, it affects both people's lives and data integrity.…”

Section: Internet Of Things Securitymentioning

confidence: 99%

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Sayed¹,

Shoaib²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to twodimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks.

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Section: Internet Of Things Securitymentioning

confidence: 99%

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Sayed¹,

Shoaib²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…In cases where the integrity of these IoT devices is breached, hackers can use them to form a botnet that can be used as a means of attacking other systems. These IoT botnets can be used to threaten the availability of key servers via a distributed denial of service (DDoS); to spam others; to 'brick' the IoT device so that it no longer works properly; to steal sensitive information; and to mine for cryptocurrencies; or the IoT devices can even be used to cause physical harm when devices are used maliciously in some contexts (Schiller et al, 2022). In all of these cases, the greater the size of the data collected and the greater the extent to which these data collection systems are imbricated in the basic functions of our life, the greater the target richness there is to be gained by illegitimate access.…”

Section: Target Richnessmentioning

confidence: 99%

Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness

Curran

2023

Big Data & Society

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Zuboff's The Age of Surveillance Capitalism provides a powerful analysis of the emergence of surveillance capitalism as a particular type of informational capitalism. Many of the important impacts of this project of creating larger and more integrated systems of ‘behavioural surplus’ are captured powerfully by Zuboff; yet as different risk and organisational scholars such as Beck, Perrow, and Vaughan have argued, integrated systems often do not function as intended. While the imperfection of these systems may raise the possibility that surveillance capitalism may not be as bad as Zuboff suggests, there is also a way in which these systems not functioning as intended can make surveillance capitalism an even more dystopian possibility. In this vein, this paper asks: what are the consequences when the tools of a surveillance capitalist society break down? This paper argues that it is by thinking through Zuboff's framework that we can identify the systemic fragility of a surveillance capitalist society. This systemic fragility emerges through how surveillance capitalism generates imperatives towards the maximal collection of data for exploitation, which in turn generates a corresponding imperative to connect all aspects of life. Both of these imperatives, of collect and connect, in turn create an immensely fragile digital system, which has vast ramifications throughout social life, such that small imperfections and gaps in the system can magnify risk throughout society.

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“…To the best our knowledge, the IoT security in recent years is based on two ways [43], machine learning [23,29,31,34,39] or trusted execution environments [47,48,51]. These approaches often utilize a central server to control resource-constrained IoT devices outside of them.…”

Section: Related Workmentioning

confidence: 99%

IoT-REX: A Secure Remote-Control System for IoT Devices from Centralized Multi-Designated Verifier Signatures

Watanabe¹,

Yanai²,

Shikata³

2022

Preprint

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IoT technology has been developing rapidly, while at the same time, notorious IoT malware such as Mirai is a severe and inherent threat. We believe it is essential to consider systems that enable us to remotely control infected devices in order to prevent or limit malicious behaviors of infected devices. In this paper, we design a promising candidate for such remote-control systems, called IoT-REX (REmote-Control System for IoT devices). IoT-REX allows a systems manager to designate an arbitrary subset of all IoT devices in the system and every device can confirm whether or not the device itself was designated; if so, the device executes a command given from the systems manager. Towards realizing IoT-REX, we introduce a novel cryptographic primitive called centralized multi-designated verifier signatures (CMDVS). Although CMDVS works under a restricted condition compared to conventional MDVS, it is sufficient for realizing IoT-REX. We provide an efficient CMDVS construction from any approximate membership query structures and digital signatures, yielding compact communication sizes and efficient verification procedures for IoT-REX. We then discuss the feasibility of IoT-REX through cryptographic implementation of the CMDVS construction on a Raspberry Pi. Our promising results demonstrate that the CMDVS construction can compress communication size to about 30% and thus its resulting IoT-REX becomes three times faster than a trivial construction over typical low-power wide area networks with an IoT device. It is expected that IoT-REX can control 12,000 devices within a second.

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Landscape of IoT security

Cited by 114 publications

References 45 publications

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness

IoT-REX: A Secure Remote-Control System for IoT Devices from Centralized Multi-Designated Verifier Signatures

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