Network Decoy Emulation for Real-Time Malware Detection
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Solution Overview
Problem
Current cybersecurity systems, such as antivirus software and firewalls, are limited in their ability to detect malware in real-time and are not effective against new or unknown threats, as they rely on predefined signatures and port blocking, respectively.
Innovation Solution
A method and system that automatically discovers resources on a network, emulates these resources, and uses malware trap sensors to detect and analyze malware interactions, uploading data for analysis and taking actions based on the results, while also monitoring network traffic and assigning severity scores to detected payloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If antivirus software performs periodic scans, then malware detection is achieved, but real-time detection capability is lost
Solution Approach 1:
The system preemptively deploys decoy resources (fake files, emulated services) throughout the network before malware arrives. These pre-positioned traps are designed to attract and capture malware attempts, enabling detection the moment malware interacts with any resource rather than waiting for scheduled scans.
Solution Approach 2:
Decoy resources serve as intermediaries between the security system and actual valuable assets. Malware interacts with these harmless fake resources instead of real ones, allowing the system to study malware behavior in controlled environments and detect threats before they reach critical systems.
2Reliability
If antivirus software uses predefined malware signatures, then known malware detection is achieved, but detection of new unknown malware is prevented
Solution Approach 1:
The system allows malware to freely interact with decoy resources, converting potentially harmful malware activity into beneficial detection opportunities. By analyzing how malware behaves when attempting to access or modify decoy files and services, the system learns patterns of malicious behavior that indicate new threat types without requiring prior knowledge of specific malware signatures.
Solution Approach 2:
The system automatically analyzes malware behavior patterns observed during interactions with decoy resources and uses this information to detect and respond to new threat types. The decentralized architecture allows each node to independently learn from local malware encounters and contribute to overall system intelligence.
3Ease of operation
If firewalls block predefined ports, then access control is achieved, but prevention of malware using open ports is lost
Solution Approach 1:
The system segments the network into multiple zones with decoy resources distributed throughout. Rather than relying on perimeter-based port blocking, each segment monitors its own traffic patterns and malware interactions independently, allowing granular detection of malicious activities regardless of which ports are open.
4Reliability
If emulated resources are deployed throughout the network, then malware detection coverage is improved, but system complexity increases
Solution Approach 1:
The system creates simplified copies of valuable network resources as decoys - fake files mimicking real documents, emulated services replicating common applications. These copies contain minimal functionality needed to attract malware interactions while remaining computationally lightweight and easy to deploy across numerous network nodes.
Data Source
AI summary
A system and method for detecting a cyber-threat according to embodiments of the present invention comprise automatically discovering resources on a network, by a resource detection unit, emulating, by a faked asset creation unit, at least one resource discovered on the network, associating a malware trap sensor with the emulated resource and detecting by the malware trap sensor, a malware related to the emulated resource. The system and method may further comprise uploading data related to the detected malware to a server, analyzing, by the server, uploaded data to produce an analysis result and perform one or more actions based on the analysis result.