A cross-layer cooperative jamming scheme for social internet of things

Huo, Yan; Fan, Jiangwen; Wen, Yingyou; Li, Ruinian

doi:10.26599/tst.2020.9010020

Cited by 18 publications

(9 citation statements)

References 47 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, jamming detection based on machine learning algorithms are popular [13]. In [14], Cross layer based jamming detection for IoT using artificial intelligence is explored. Jamming detection based on random forest, support vector machine, and neural network for 5G network is carried out in [15].…”

Section: Related Workmentioning

confidence: 99%

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

Ganeshkumar¹,

Albalawi²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Internet of things (IoT) comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life. Generally, IoT nodes are connected in wireless media and thus they are prone to jamming attacks. In the present scenario jamming detection (JD) by using machine learning (ML) algorithms grasp the attention of the researchers due to its virtuous outcome. In this research, jamming detection is modelled as a classification problem which uses several features. Using one/two or minimum number of features produces vague results that cannot be explained. Also the relationship between the feature and the class label cannot be efficiently determined, specifically, if the chosen number of features for training is minimum (say 1 or 2). To obtain good results, machine-learning algorithms are trained by large number of data sets. However, collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic. In this paper, to solve this problem, more number of features with nominal number of data's is considered that eases the data collection and the classification accuracy. In this research, an efficient technique based on locality sensitive hashing (LSH) for K-nearest neighbor algorithm (K-NN), which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated. From the results, it is clear that the obtained results are validatable and the model is more sensible.

show abstract

Section: Related Workmentioning

confidence: 99%

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

Ganeshkumar¹,

Albalawi²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In addition, the possibility of defects in the security design might multiply the numbers and type of the IoT devices. With so many linked IoT devices, any poorly protected online device may impair remote and global Internet safety and resilience [83]. For instance, a malware-infected unprotected refrigerator or television (TV) in the US can send thousands of spam emails to the world via the homeowners' Wi-Fi network [14].…”

Section: Iot Implementation Challengesmentioning

confidence: 99%

Internet of Things-Building Information Modeling Integration: Attacks, Challenges, and Countermeasures

et al. 2022

View full text Add to dashboard Cite

“…Elliptic curve cipher is recognized as the most secure encryption algorithm for a given length of secret key; it is profoundly influencing the technological development of industrial IoT such as intelligent monitoring [25], data flow management [26], and data prediction [27]. Elliptic curve cipher is a public key encryption algorithm based on the elliptic curve.…”

Section: Elliptic Curve Codementioning

confidence: 99%

Blockchain-Enhanced Fair and Efficient Energy Trading in Industrial Internet of Things

Chen

Ren

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

As the technical support of the industrial Internet of Things, blockchain technology has been widely used in energy trading, data transactions, and Internet of Vehicles. However, most of the existing energy trading models only address the transaction security and transaction privacy issues that arise in the energy trading process, ignoring the fairness of resource allocation and transaction equity in the trading process. In order to tackle those problems, an energy trading scheme called HO-TRAD is proposed in this paper to improve the efficiency of model trading while ensuring the fairness of energy trading. We propose a new trading strategy in the HO-TRAD energy trading scheme that guarantees fairness in the allocation of trading resources by introducing an entity’s active reputation value. Use smart contracts to achieve transparency and ensure fairness in the transaction process. Based on the identity verification foundation of the consortium chain, the scheme enhances the existing PBFT consensus algorithm and improves the efficiency of model transactions. The experimental simulation indicates that the scheme requires less transaction time and has higher transaction fairness and security.

show abstract

A cross-layer cooperative jamming scheme for social internet of things

Cited by 18 publications

References 47 publications

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

Internet of Things-Building Information Modeling Integration: Attacks, Challenges, and Countermeasures

Blockchain-Enhanced Fair and Efficient Energy Trading in Industrial Internet of Things

Contact Info

Product

Resources

About