Network Application Firewall Honeytrap Deception
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Solution Overview
Problem
Existing network security systems face challenges in distinguishing between legitimate and malicious users, leading to potential false positives and reduced security effectiveness, as well as impacting user experience and revenue due to cumbersome safeguards.
Innovation Solution
Implementing a network application firewall that uses honeytraps and deception logic to proactively detect and divert malicious users by embedding deceptive payloads in responses, simulating vulnerabilities, and using machine learning to enhance protection while conserving primary computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security systems use cumbersome safeguards to detect malicious users, then security detection capability is improved, but user experience deteriorates and false positives increase
Solution Approach 1:
The system performs preliminary actions by embedding honeytraps (deceptive payloads) in responses before malicious activity occurs. These honeytraps contain fake vulnerability information that malicious users may attempt to exploit later. When a user interacts with the honeytrap, their malicious intent is revealed, enabling accurate detection without impacting legitimate users' experience.
Solution Approach 2:
The honeytrap acts as an intermediary element between the security system and the malicious user. Instead of directly confronting or blocking users, the system inserts deceptive content that mediates the interaction - legitimate users ignore it while malicious users attempt to exploit it, thereby revealing their true nature without disrupting normal user experience.
2Measurement precision
If network security systems implement proactive deception logic with honeytraps, then malicious activity detection precision is improved, but system complexity increases
Solution Approach 1:
The honeytrap is nested within the legitimate response content. The deceptive payload is embedded inside the normal application response, creating a nested structure where the outer layer is legitimate content and the inner layer contains the honeytrap. This nesting approach allows the system to maintain normal functionality while incorporating detection mechanisms, thereby managing complexity.
Solution Approach 2:
The system creates copies of legitimate response content and embeds honeytraps within these copies. Rather than creating entirely new complex detection systems, the firewall replicates normal responses and inserts deceptive elements, simplifying the overall system architecture while maintaining high detection precision.
3Reliability
If traditional firewalls block all suspicious traffic, then security protection is improved, but legitimate traffic may be blocked causing loss of revenue
Solution Approach 1:
The system performs preliminary detection by embedding honeytraps before blocking any traffic. Only after a user demonstrates malicious behavior by attempting to exploit the honeytrap does the system take action. This preliminary action ensures that legitimate traffic flows uninterrupted while malicious traffic is identified and blocked, maintaining both security protection and productivity.
Solution Approach 2:
The honeytrap enables malicious users to self-identify through their own actions. When a user attempts to exploit the deceptive payload, they automatically reveal their malicious intent without requiring external intervention or complex analysis. This self-service mechanism allows the system to distinguish malicious from legitimate traffic with high accuracy, preventing false positives that would block legitimate traffic.
Data Source
AI summary
Technology related to a network application firewall is disclosed. In one example, a method includes intercepting a response from a network application and destined for a client. The response can be associated with a user identifier. A modified response can be forwarded to the client. The modified response can include a honeytrap embedded within the intercepted response. Engagement with the honeytrap can be detected in a subsequent request to the network application. In response to detecting the engagement with the honeytrap, an indication that the user identifier is malicious can be stored.