Naik, Nitin, Jenkins, Paul, Savage, Nick and Yang, Longzhi (2021) A computational intelligence enabled honeypot for chasing ghosts in the wires. Complex & Intelligent Systems, 7 (1). pp. 477-494. ISSN 2199-4536
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Abstract
A honeypot is a concealed security system that functions as a decoy to entice cyberattackers to reveal their information. Therefore, it is essential to disguise its identity to ensure its successful operation. Nonetheless, cyberattackers frequently attempt to uncover these honeypots; one of the most effective techniques for revealing their identity is a fingerprinting attack. Once identified, a honeypot can be exploited as a zombie by an attacker to attack others. Several effective techniques are available to prevent a fingerprinting attack, however, that would be contrary to the purpose of a honeypot, which is designed to interact with attackers to attempt to discover information relating to them. A technique to discover any attempted fingerprinting attack is highly desirable, for honeypots, while interacting with cyberattackers. Unfortunately, no specific method is available to detect and predict an attempted fingerprinting attack in real-time due to the difficulty of isolating it from other attacks. This paper presents a computational intelligence enabled honeypot that is capable of discovering and predicting an attempted fingerprinting attack by using a Principal components analysis and Fuzzy inference system. This proposed system is successfully tested against the five popular fingerprinting tools Nmap, Xprobe2, NetScanTools Pro, SinFP3 and Nessus.
Item Type: | Article |
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Uncontrolled Keywords: | Cyberattack, Honeypot, Computational intelligence, Fingerprinting attack, Principal components analysis, Fuzzy inference system |
Subjects: | G400 Computer Science G500 Information Systems G600 Software Engineering G700 Artificial Intelligence |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Elena Carlaw |
Date Deposited: | 04 Jan 2022 12:19 |
Last Modified: | 04 Jan 2022 12:30 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/48066 |
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