Polymorphic Honeypot Deception for Adaptive Network Attack Defense
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Solution Overview
Problem
Existing network monitoring systems face challenges in effectively thwarting attacks due to the scale and dynamic nature of modern networks, the sophistication of attackers, and the need for continuous updates in deception techniques.
Innovation Solution
Implementing network monitoring computers (NMCs) that passively monitor network traffic, classify anomalous events, and deploy honeypot traps mimicking network targets to lure attackers, allowing for real-time adaptation and monitoring of attack strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional passive network monitoring is used, then network traffic can be observed, but attacks cannot be effectively thwarted due to the sophistication of attackers and dynamic nature of modern networks
Solution Approach 1:
The system performs preliminary actions by proactively deploying honeypot traps before actual attacks occur. The classification engine pre-identifies potential attack vectors and the honeypot trap engine pre-deploys corresponding deceptive targets, allowing the system to intercept and neutralize attacks before they reach real assets.
Solution Approach 2:
The system implements dynamic adaptation through continuous monitoring and real-time classification of network traffic. The honeypot traps are dynamically generated and deployed based on current threat patterns, allowing the defense mechanism to evolve alongside attacker sophistication rather than relying on static signatures.
2Reliability
If deception techniques are updated continuously to match attacker sophistication, then defense effectiveness improves, but system complexity and resource requirements increase
Solution Approach 1:
The system achieves self-service through automated classification and generation of honeypot traps. The classification engine automatically analyzes network traffic to identify attack patterns, and the honeypot trap engine automatically generates and deploys appropriate deceptive targets without requiring manual intervention or complex configuration updates.
Solution Approach 2:
The honeypot trap engine serves multiple functions: it classifies attack types, generates diverse honeypot traps for different protocols and services, deploys traps across the network, and monitors trap interactions. This multi-functionality consolidates what would otherwise require separate specialized systems into a single unified platform.
3Reliability
If honeypot traps are deployed to lure attackers, then attack mitigation improves, but the system requires active participation rather than passive monitoring
Solution Approach 1:
The system automates the entire honeypot deployment process through self-service mechanisms. The classification engine autonomously analyzes incoming traffic to detect attacks, determines appropriate honeypot trap types, and triggers automatic deployment by the honeypot trap engine, eliminating the need for manual operational intervention.
Solution Approach 2:
The system implements closed-loop feedback where the results of honeypot trap interactions are continuously monitored and fed back to the classification engine. This feedback mechanism allows the system to learn from actual attack patterns and refine its classification and trap deployment strategies, improving operational efficiency over time.
Data Source
AI summary
Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Anomalous events may be classified based on the monitored network traffic and attack models such that the classification determines that targets of the anomalous events may be currently subject to attacks by entities communicating on the networks. A honeypot trap may be provided in the networks based on the classified events such that the honeypot trap mimics characteristics of the targets. The portions of the network traffic associated with the honeypot trap may be monitored. Characteristics of the attacks may be determined based on the monitored portions of network traffic. Reports that include information based on the characteristics of the attacks may be generated.