SoProtector: Safeguard Privacy for Native SO Files in Evolving Mobile IoT Applications

Xu, Guangquan; Wang, Weizhe; Jiao, Litao; Li, Xiaotong; Liang, Kaitai; Zheng, Xi; Lian, Wenjuan; Xian, Hequn; Gao, Honghao

doi:10.1109/jiot.2019.2944006

Cited by 20 publications

(9 citation statements)

References 31 publications

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“…To evaluate the effectiveness of the proposed data reconstruction method, we performed a CS‐based data reconstruction experiment based on different reconstruction algorithms 34‐36 . The data reconstruction process of this method has two phases.…”

Section: Methodsmentioning

confidence: 99%

Trusted data collection for Internet of Things

Ning

Gong

et al. 2021

Concurrency and Computation

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Summary Trusted date collection is the precondition of the security of Internet of Things (IOT). Recently, compressed sensing technology, which can enable the reduction of the energy consumption and time delay of data collection, has been extensively studied in IOT; However, the security of compressed sensing technology in IOT has not been sufficiently considered. Since compressed sensing depends the cooperation between IOT nodes and then sensing data is transparent for all IOT nodes, which is subjected to malicious nodes attack. Therefore, we propose a trusted data collection scheme based on compressed sensing oriented to the IOT. When data collection is tampered with, the scheme can detect malicious nodes. Experiments demonstrate that the proposed scheme can ensure data security with a low energy consumption.

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Section: Methodsmentioning

confidence: 99%

Trusted data collection for Internet of Things

Ning

Gong

et al. 2021

Concurrency and Computation

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“…On the one hand, the limited computational capability and battery capacity lead to the difficulty of deploying computationally-complicated encryption algorithms at IIoT devices. Meanwhile, the failure of upgrading or patching IoT firmware also results in the IIoT devices being vulnerable to malicious attacks [157]. It is reported in [158] that blockchain-enabled smart contracts can automate the IoT firmware upgrading procedure through the contract clauses (i.e., instructions) built-in IoT devices since the date of production.…”

Section: B Secure Iiot Critical Infrastructuresmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

214

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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“…22 Other similar work can refer to previous studies. [6][7][8][23][24][25][26] Currently, with the increase in grid size and scale, SG needs more resources than traditional electronic grid. Due to this, many approaches start using cloud computing to enhance the performance.…”

Section: Related Workmentioning

confidence: 99%

A fog‐based collaborative intrusion detection framework for smart grid

Wang

2020

Int J Network Mgmt

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With the rapid development of information and communication technologies (ICTs), the conventional electrical grid is evolving towards an intelligent smart grid. Due to the complexity, how to protect the security of smart grid environments still remains a practical challenge. Currently, collaborative intrusion detection systems (CIDSs) are one important solution to help identify various security threats, through allowing various IDS nodes to exchange data and information. However, with the increasing adoption of ICT in smart grid, cloud computing is often deployed in order to reduce the storage burden locally. However, due to the distance between grid and cloud, it is critical for smart grid to ensure the timely response to any accidents. In this work, we review existing collaborative detection mechanisms and introduce a fog-based CIDS framework to enhance the detection efficiency. The results show that our approach can improved the detection efficiency by around 21% to 45% based on the concrete attacking scenarios.

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SoProtector: Safeguard Privacy for Native SO Files in Evolving Mobile IoT Applications

Cited by 20 publications

References 31 publications

Trusted data collection for Internet of Things

Trusted data collection for Internet of Things

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

A fog‐based collaborative intrusion detection framework for smart grid

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