Intelligent IoT Honeypot Using ML to Simulate Device Responses
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Solution Overview
Problem
Existing honeypot technologies are inadequate for IoT devices due to their heterogeneity, making manual low-interaction honeypots inefficient and high-interaction honeypots costly and risky to deploy, as they require physical devices and complex emulators.
Innovation Solution
An intelligent-interaction honeypot system that uses automated machine learning to simulate IoT device behaviors by actively probing physical devices, collecting responses, and leveraging machine learning to optimize reply logic, allowing for efficient and cost-effective emulation of IoT devices without the need for physical hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual low-interaction honeypots are used for IoT devices, then device complexity and deployment cost are reduced, but interaction fidelity and vulnerability detection capability deteriorate
Solution Approach 1:
The patent creates virtual copies of IoT devices through honeypots that replicate device behaviors, network responses, and interaction patterns. These virtual honeypots copy the essential characteristics of target IoT devices without requiring physical hardware, enabling accurate vulnerability detection while maintaining low deployment complexity
Solution Approach 2:
The system dynamically adjusts honeypot interaction parameters such as response timing, protocol versions, and device behavior patterns to match real IoT devices. By changing these parameters based on observed traffic patterns and device fingerprints, the honeypot achieves high interaction fidelity without requiring complex physical emulations
2Measurement precision
If high-interaction honeypots with physical devices are deployed, then interaction fidelity improves, but deployment cost and security risk increase
Solution Approach 1:
The patent introduces an intermediary layer between attackers and real IoT devices through virtual honeypots. These honeypots act as mediators that simulate vulnerable device behaviors, allowing security researchers to study attack patterns and detect vulnerabilities without exposing actual physical devices to security risks
Solution Approach 2:
The system uses disposable virtual honeypot instances that can be rapidly deployed and discarded. These temporary virtual devices provide sufficient interaction fidelity for vulnerability detection but can be easily reset or replaced, minimizing the security risk associated with maintaining persistent high-interaction honeypots
3Measurement precision
If automated machine learning is used to simulate IoT behaviors, then interaction fidelity improves, but system complexity and computational resources increase
Solution Approach 1:
The honeypot system uses automated machine learning algorithms that self-train by observing real IoT device traffic patterns. The system automatically collects data from captured packets, learns device behaviors and protocols, and improves its simulation accuracy over time without requiring manual configuration or complex rule-based systems
Solution Approach 2:
The patent replaces complex manual configuration systems with automated machine learning models. Instead of manually programming honeypot behaviors and responses, the system uses ML algorithms to automatically learn and replicate IoT device interactions, reducing system complexity while improving behavior simulation accuracy
4Measurement precision
If session length is extended to capture exploit attempts, then vulnerability detection capability improves, but resource consumption and false positive rate increase
Solution Approach 1:
The system extends session monitoring only partially - specifically for connections showing signs of exploitation attempts. Rather than monitoring all connections indefinitely, the honeypot uses initial behavior analysis to identify suspicious sessions and extends monitoring only for those cases, reducing overall resource consumption while maintaining high exploit detection accuracy
Data Source
AI summary
Techniques for providing an intelligent-interaction honeypot for IoT devices in accordance with some embodiments. In some embodiments, a system/process/computer program product for providing an intelligent-interaction honeypot for IoT devices includes receiving a request from an attacker sent to an IP address that is associated with a honeypot instance for Internet of Things (IoT) devices; determining a response to the request using a data store that stores a plurality of responses and associated IoT device information, wherein the plurality of responses and associated IoT device information is generated based on automated machine learning of active probing of physical IoT devices on the Internet; and sending the response from the honeypot instance for IoT devices to the attacker, wherein the attacker is unable to detect that the response is associated with an emulated IoT device.