Toward trust based protocols in a pervasive and mobile computing environment: A survey

Usman, Aminu Bello; Gutiérrez, Jairo

doi:10.1016/j.adhoc.2018.07.009

Cited by 12 publications

(6 citation statements)

References 106 publications

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“…Different types of attack in the IoT environment are described in ( Ahmad & Salah, 2017 ). Usman & Gutierrez (2018) categorised the author‘s focus on wormhole attack and as well as other attacks are reviewed in ( Farjamnia, Gasimov & Kazimov, 2019 ). Finally, the attack is categorised according to the attack method and how the malicious node can achieve its objective.…”

Section: Survey Methodologymentioning

confidence: 99%

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Arshad

Hanapi

Subramaniam

et al. 2021

PeerJ Computer Science

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Wireless sensor networks (WSN) have been among the most prevalent wireless innovations over the years exciting new Internet of Things (IoT) applications. IoT based WSN integrated with Internet Protocol IP allows any physical objects with sensors to be connected ubiquitously and send real-time data to the server connected to the Internet gate. Security in WSN remains an ongoing research trend that falls under the IoT paradigm. A WSN node deployed in a hostile environment is likely to open security attacks such as Sybil attack due to its distributed architecture and network contention implemented in the routing protocol. In a Sybil attack, an adversary illegally advertises several false identities or a single identity that may occur at several locations called Sybil nodes. Therefore, in this paper, we give a survey of the most up-to-date assured methods to defend from the Sybil attack. The Sybil attack countermeasures includes encryption, trust, received signal indicator (RSSI), encryption and artificial intelligence. Specifically, we survey different methods, along with their advantages and disadvantages, to mitigate the Sybil attack. We discussed the lesson learned and the future avenues of study and open issues in WSN security analysis.

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Section: Survey Methodologymentioning

confidence: 99%

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Arshad

Hanapi

Subramaniam

et al. 2021

PeerJ Computer Science

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“…Pervasive and mobile intelligence all velopment of new applications and systems and significantly expands the ra puting opportunities, including digital intelligence in everyday items and ho simplify and improve efficiency [26]. Usman and Gutierrez [27] Deep Learning (DL): Solving urban problems may require a deep learning approach to generate large values through urban computing. Many studies have incorporated deep learning into smart city applications to uncover new information from big urban data [15,20].…”

Section: State Of the Current Researchmentioning

confidence: 99%

Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges

et al. 2023

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The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly.

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“…𝛼 𝑟𝑒𝑐 (𝑡 + 1) = 𝛼 𝑟𝑒𝑐 (𝑡) × 𝜏 𝛼 (𝑡) + 𝛼 𝑟𝑝𝑒𝑟𝑖𝑜𝑑 (10) 𝛽 𝑟𝑒𝑐 (𝑡 + 1) = 𝛽 𝑟𝑒𝑐 (𝑡) × 𝜏 𝛽 (𝑡) + 𝛽 𝑟𝑝𝑒𝑟𝑖𝑜𝑑 (11) International Journal of Intelligent Engineering and Systems, Vol. 16…”

Section: ) Referral Trust Updates Without Cb Attackmentioning

confidence: 99%

“…The potential of many elements for evaluating confidence opens up a wide range of study prospects. This motivates the scholars to contribute by creating and implementing a trustbased routing system in the AODV framework [11].…”

Section: Introductionmentioning

confidence: 99%

Conflicting Behaviour Attack Prediction using Machine Learning for Mobile Adhoc Network

2023

IJIES

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Mobile adhoc network (MANET) is a type of wireless configuration that features self-organizing wireless mobile nodes and adaptive network connection. Security is a key concern for MANETs due to their dynamic nature and continually changing topology. To improve security, an adaptive trust threshold-aware secure energy-efficient protocol was designed, which adaptively predicts the threshold value using an artificial neural network (ANN) to evaluate the node's trust for detecting and preventing the suspected nodes. In contrast, it fails to detect and mitigate conflicting behavior (CB) attacks, in which the suspected node may behave well towards a specific group of nodes and badly towards another group of nodes. Therefore, this article proposes a CB attack prediction using the shared learning-based ANN (CBAP-SLANN) algorithm to predict CB attacks in different nodes as well as different timeslots within the same node. The trust calculation should then take into account the consistency of behavior when employing different node-based observations along with the proposed different time-based observations. Initially, nodes are divided into overlapping clusters, and the trust and various network parameters of each node are observed for every group individually. Then, the ANN algorithm is trained in each group using the observed parameters and the trained model for each group is combined to get the global decision, which helps to predict the CB attack nodes in the network. At last, the simulation outcomes show that the CBAP-SLANN algorithm attains 93.1% accuracy when deploying 300 nodes compared to the trust-based routing algorithms.

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Toward trust based protocols in a pervasive and mobile computing environment: A survey

Cited by 12 publications

References 106 publications

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges

Conflicting Behaviour Attack Prediction using Machine Learning for Mobile Adhoc Network

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