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.