Citation

Ahmad Zukarnain, Zuriati and Amodu, Oluwatosin Ahmed and Wenting, Cui and Bukar, Umar Ali (2023) A Survey of Sybil Attack Countermeasures in Underwater Sensor and Acoustic Networks. IEEE Access, 11. pp. 64518-64543. ISSN 2169-3536

Text 10.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Underwater sensor and acoustic networks have several unique applications including water quality and ocean life monitoring, as well as ocean navigation and exploration. They also have peculiar physical layer characteristics with respect to operating frequency and attenuation which makes them different from terrestrial wireless sensor communication. Thus, coupled with their large cost of deployment and sensitivity, they are highly vulnerable to security attacks. For instance, a Sybil node could pretend to be at several other locations in the sparse network simultaneously, thereby deceiving legitimate nodes and infringing on the security of transmitted information. Over the last few years, researchers have studied means of preventing, detecting, and mitigating Sybil attacks for safe underwater communication under different assumptions and architectural setups. However, to our knowledge, these efforts have been scattered in the literature and concrete lessons have not been drawn from these efforts via a survey/review on this subject towards achieving safe underwater communication. This motivates the presentation of this paper that provides an exposition of the academic discussion on the solutions for addressing Sybil attacks in underwater wireless communication, with respect to attack prevention, detection and mitigation while identifying some of their limitations. Similarly, proposed methods and technical aspects peculiar to these works are identified, and a wide range of challenges, opportunities, and recommendations are provided.

Item Type:	Article
Uncontrolled Keywords:	Networks
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2023 01:40
Last Modified:	01 Aug 2023 01:40
URII:	http://shdl.mmu.edu.my/id/eprint/11589

Downloads

Edit (login required)