ConformityGate: Intelligent Honeypot-Based Intrusion Detection Framework for Secure IoT Networks

Abstract: More gadgets connected online - hospitals, factories, cities - are opening new doors for hackers. Old security tools struggle to keep up. A fresh approach called ConformityGate steps in here. Instead of just blocking attacks, it guides them away quietly. Suspicious behavior triggers a hidden shift. Traffic gets rerouted without warning. Fake setups wait there, watching everything the attacker does. These decoys collect real-time details on how intruders operate. Behind the scenes, Python code runs the show. It talks to sensors scattered through the network. Data streams in nonstop: logins, device status, traffic patterns. When something odd crosses set limits, action follows fast. No loud alarms. Just smooth isolation. Alerts spread like ripples across linked devices. Evidence stays intact. Moves stay unseen. One test after another, through five fake IoT setups, showed it spots attacks 96.7% of the time. False alarms pop up just 2.9% of the time. Responses take an average of 1.6 seconds to kick in. That speed and precision beats older methods relying on signatures, odd behavior patterns, or machine learning models. Built tough but simple, ConformityGate fits real-world needs. It scales without breaking down. Works across many kinds of smart devices. Protection stays strong even when threats get clever. This isn’t theory - it runs where it matters.

Internet of Things security using honeypots and anomaly detection with cyber threat intelligence in network environments through transparent redirection and intrusion detection systems implemented in Python