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TwinPot: Digital Twin-assisted Honeypot for Cyber-Secure Smart Seaports

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Is a
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Academic paper
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Academic Paper attributes

arXiv ID
2310.128800
arXiv Classification
Computer science
Computer science
0
Publication URL
arxiv.org/pdf/2310.1...80.pdf0
Publisher
ArXiv
ArXiv
0
DOI
doi.org/10.48550/ar...10.128800
Paid/Free
Free0
Academic Discipline
Artificial Intelligence (AI)
Artificial Intelligence (AI)
0
Computer science
Computer science
0
Computer network
Computer network
0
Submission Date
October 19, 2023
0
October 25, 2023
0
Author Names
Yagmur Yigit0
Omer Kemal Kinaci0
Trung Q. Duong0
Berk Canberk0
Paper abstract

The idea of next-generation ports has become more apparent in the last ten years in response to the challenge posed by the rising demand for efficiency and the ever-increasing volume of goods. In this new era of intelligent infrastructure and facilities, it is evident that cyber-security has recently received the most significant attention from the seaport and maritime authorities, and it is a primary concern on the agenda of most ports. Traditional security solutions can be applied to safeguard IoT and Cyber-Physical Systems (CPS) from harmful entities. Nevertheless, security researchers can only watch, examine, and learn about the behaviors of attackers if these solutions operate more transparently. Herein, honeypots are potential solutions since they offer valuable information about the attackers. It can be virtual or physical. Virtual honeypots must be more realistic to entice attackers, necessitating better high-fidelity. To this end, Digital Twin (DT) technology can be employed to increase the complexity and simulation fidelity of the honeypots. Seaports can be attacked from both their existing devices and external devices at the same time. Existing mechanisms are insufficient to detect external attacks; therefore, the current systems cannot handle attacks at the desired level. DT and honeypot technologies can be used together to tackle them. Consequently, we suggest a DT-assisted honeypot, called TwinPot, for external attacks in smart seaports. Moreover, we propose an intelligent attack detection mechanism to handle different attack types using DT for internal attacks. Finally, we build an extensive smart seaport dataset for internal and external attacks using the MANSIM tool and two existing datasets to test the performance of our system. We show that under simultaneous internal and external attacks on the system, our solution successfully detects internal and external attacks.

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