Smart Proxy Honeypot Farm for Advanced Threat Detection
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Solution Overview
Problem
Existing security measures, including honeypot solutions and virtual machine environments, are inadequate in detecting advanced persistent threats (APT) and zero-day exploits due to their inability to provide a realistic emulation of a target host and network environment, leading to incomplete threat detection and intelligence gathering.
Innovation Solution
A large scale high-interaction honeypot farm system that uses a smart proxy to forward live attack traffic to a honeypot cloud, where it is matched with vulnerable service instances based on deep packet inspection, and executed in an instrumented virtual environment with hybrid intrusion detection to identify threats and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing honeypot solutions and virtual machine environments are used, then basic network security is maintained, but they fail to detect advanced persistent threats and zero-day exploits due to inability to provide realistic emulation
Solution Approach 1:
The patent creates a copy of the target network environment by deploying honeypot instances that replicate vulnerable services and systems. These honeypots are distributed across multiple geographic locations and emulate real-world network configurations, allowing attackers to interact with realistic environments while actual production systems remain protected. This copying approach enables reliable detection of advanced threats without exposing real systems.
Solution Approach 2:
The patent segments the honeypot infrastructure into multiple independent instances distributed across different geographic locations and network zones. Each honeypot instance can be independently configured with specific vulnerability profiles, allowing the system to detect various types of threats simultaneously. This segmentation also isolates attack impacts, preventing single-point failures and enabling versatile threat detection across diverse attack vectors.
2Reliability
If a large scale honeypot farm is deployed to provide realistic emulation, then advanced threat detection improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent designs honeypot instances with multi-functionality, where each honeypot can serve multiple detection purposes simultaneously. The same honeypot infrastructure detects various threat types including APTs, zero-day exploits, and common malware through configurable vulnerability profiles. This universal approach reduces overall system complexity by consolidating multiple specialized honeypots into versatile, multi-purpose instances that maintain high detection reliability.
Solution Approach 2:
The patent introduces a smart proxy as an intermediary component that manages traffic between the external network and the honeypot farm. The smart proxy performs deep packet inspection, routes traffic to appropriate honeypot instances based on attack patterns, and aggregates detection data. This intermediary layer simplifies the complexity of managing large-scale honeypot infrastructure by providing centralized control and automated traffic management.
3Measurement precision
If smart proxy with deep packet inspection is used to forward traffic to honeypots, then threat matching accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring honeypot instances with specific vulnerability profiles and service configurations before attacks occur. The smart proxy is pre-loaded with knowledge of various attack patterns and routing rules. When traffic arrives, the matching process leverages these pre-established configurations to quickly identify and route attacks to appropriate honeypots, maintaining high matching accuracy while reducing real-time processing time.
Solution Approach 2:
The patent applies dynamics by making the smart proxy's traffic routing capabilities adaptive and flexible. The system dynamically adjusts routing decisions based on real-time attack patterns, honeypot availability, and detected threat characteristics. This dynamic approach allows the system to optimize processing time while maintaining high matching accuracy by adapting to changing conditions rather than relying on static routing rules.
Data Source
AI summary
Techniques for providing a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a large scale high-interaction honeypot farm includes sending traffic detected at a sensor to a smart proxy for a honeypot farm that is executed in a honeypot cloud, wherein the traffic is forwarded attack traffic that is sent using a tunneling protocol, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; forwarding the traffic to an instance of the matching type of vulnerable service; and executing a security agent associated with the instance of the matching type of vulnerable service to identify a threat by monitoring behaviors and detecting anomalies or post exploitation activities.