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. 2022 Sep 8;22(18):6806.
doi: 10.3390/s22186806.

DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology

Affiliations

DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology

Rahmeh Fawaz Ibrahim et al. Sensors (Basel). .

Abstract

The Internet of Things (IoT) has widely expanded due to its advantages in enhancing the business, industrial, and social ecosystems. Nevertheless, IoT infrastructure is susceptible to several cyber-attacks due to the endpoint devices' restrictions in computation, storage, and communication capacity. As such, distributed denial-of-service (DDoS) attacks pose a serious threat to the security of the IoT. Attackers can easily utilize IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws. This paper proposes an Ethereum blockchain model to detect and prevent DDoS attacks against IoT systems. Additionally, the proposed system can be used to resolve the single points of failure (dependencies on third parties) and privacy and security in IoT systems. First, we propose implementing a decentralized platform in place of current centralized system solutions to prevent DDoS attacks on IoT devices at the application layer by authenticating and verifying these devices. Second, we suggest tracing and recording the IP address of malicious devices inside the blockchain to prevent them from connecting and communicating with the IoT networks. The system performance has been evaluated by performing 100 experiments to evaluate the time taken by the authentication process. The proposed system highlights two messages with a time of 0.012 ms: the first is the request transmitted from the IoT follower device to join the blockchain, and the second is the blockchain response. The experimental evaluation demonstrated the superiority of our system because there are fewer I/O operations in the proposed system than in other related works, and thus it runs substantially faster.

Keywords: DDoS attacks; Ethereum; IoT; authorization; blockchain; smart contract.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block Structure [15].
Figure 2
Figure 2
(A) Initialization phase; (B) Communication phase.
Figure 3
Figure 3
Evaluation results: (A) CPU Association Time Per Request (ms), (B) Association Time (SD in ms), (C) Association Time Average (ms), (D) Data message Average Time (ms), and (E) Data message Time (ms).

References

    1. Otoum Y., Liu D., Nayak A. DL-IDS: A deep learning–based intrusion detection framework for securing IoT. Trans. Emerg. Telecommun. Technol. 2022;33:e3803. doi: 10.1002/ett.3803. - DOI
    1. Abu Al-Haija Q., Al-Saraireh J. Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning. Symmetry. 2022;14:591. doi: 10.3390/sym14030591. - DOI
    1. Madakam S., Ramaswamy R., Tripathi S. Internet of Things (IoT): A Literature Review. J. Comput. Commun. 2015;3:164–173. doi: 10.4236/jcc.2015.35021. - DOI
    1. Abu Al-Haija Q., Al-Badawi A. Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning. Sensors. 2022;22:241. doi: 10.3390/s22010241. - DOI - PMC - PubMed
    1. Albulayhi K., Abu Al-Haija Q., Alsuhibany S.A., Jillepalli A.A., Ashrafuzzaman M., Sheldon F.T. IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method. Appl. Sci. 2022;12:5015. doi: 10.3390/app12105015. - DOI