Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

Chen, Songlin; Pang, Zhibo; Wen, Hong; Yu, Kan; Zhang, Tengyue; Lu, Yueming

doi:10.1109/tii.2020.2963962

Cited by 55 publications

(21 citation statements)

References 19 publications

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“…is paper is a distributed replica detection program inspired by Ho et al [26]. Related research of clone node detection in MWSN can be found in [45,46].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Lalar

Bhushan

Jangra

et al. 2021

Security and Communication Networks

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Wireless sensor networks have been deployed in the open and unattended environment where the attacker can capture the sensors and create the replica of captured nodes. As the clone nodes have been considered legitimate nodes, clone nodes can initiate different network attacks. We have designed a three-phase clone node detection method named fuzzy logic clone node detection (FLCND). The first phase of FLCND checks whether any node is missing from the network or not. In the next phase, FLCND finds out whether any missing node has arisen in the network in a stipulated time. If any missing node is alive, there is a possibility the node may be cloned. The information of suspected nodes is entered into the Hot-List, which has been maintained in the network. Phase III uses the suspected list and finds out the possibility of clone node using fuzzy logic. Two different scenarios have been simulated in NS2 to evaluate FLCND. The simulation result shows that the proposed method increases the packet delivery ratio (PDR) and reduces packet loss, end-to-end delay, and energy consumption. The simulation results illustrate that the FLCND method reduces the average power consumption by 27% and increases the detection rate by 46% compared to the existing techniques.

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“…is paper is a distributed replica detection program inspired by Ho et al [26]. Related research of clone node detection in MWSN can be found in [45,46].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Lalar

Bhushan

Jangra

et al. 2021

Security and Communication Networks

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“…Even though multi-access edge computing is a promising solution for improved services of mobile providers, the security of the data in the edge nodes is a concern [51]. Several applications like connected vehicles, social media apps, healthcare related applications generate the data which is very sensitive [52].…”

Section: Motivations Of the Use Of Blockchain In Eotmentioning

confidence: 99%

Blockchain for Edge of Things: Applications, Opportunities, and Challenges

Gadekallu¹,

Pham²,

Nguyen³

et al. 2021

Preprint

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In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called BEoT that has been regarded as a promising enabler for future services and applications. In this paper, we present a state-of-the-art review of recent developments in BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home and smart grid. Security challenges in BEoT paradigm are also discussed and analyzed, with some key services such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

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“…However, authentication requires high computational resources. A scheme based on channelbased machine learning was proposed in [181] to detect both Clone and Sybil attacks. Simulations and experiments have been carried out in real environments.…”

Section: A Learning-based Countermeasuresmentioning

confidence: 99%