Honeypot-Based Computer Attack Response Service
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Solution Overview
Problem
Current methods for preventing computer attacks, such as firewalls and antivirus software, are ineffective against zero-day attacks and repeatable attacks following similar patterns, leaving vulnerabilities unaddressed.
Innovation Solution
A system and method that utilizes a honeypot to detect and monitor potential threats by simulating a computing device, accessing data within electronic files, and employing machine learning to classify and respond to incoming threats, thereby isolating and preventing the spread of malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional firewall and antivirus software are used to prevent incoming threats, then basic security protection is provided, but they are ineffective against zero-day attacks and repeatable attacks following similar patterns
Solution Approach 1:
The system performs preliminary analysis of incoming files by creating honeypots before the files are executed on the target system. This advance preparation allows the system to detect and analyze malicious behavior patterns before they can cause harm, enabling defense against zero-day attacks where traditional signatures are unavailable
Solution Approach 2:
The honeypot acts as an intermediary between the incoming potentially malicious file and the target computing device. The honeypot isolates and simulates the target environment, allowing safe analysis of file behavior without direct exposure to the actual system, thus providing adaptability against unknown threat patterns
2Adaptability or versatility
If honeypots are created to analyze incoming threats, then defense against zero-day and repeatable attacks is improved, but system complexity increases
Solution Approach 1:
Instead of analyzing files directly on the target system, the system creates simplified copies (honeypots) that replicate essential target system characteristics. These copies are easier to manage and isolate, reducing the complexity burden while maintaining analytical effectiveness for detecting sophisticated attack patterns
Solution Approach 2:
The system segments the analysis process by separating threat analysis (conducted in isolated honeypots) from target system operation. This segmentation allows complex security analysis to proceed in isolated environments without overwhelming the main system, managing complexity through functional separation
3Measurement precision
If machine learning is employed to classify and respond to threats, then response accuracy to potential threats is improved, but processing time increases
Solution Approach 1:
The machine learning model performs partial analysis initially, classifying only the most critical threat indicators first. This staged approach provides sufficient accuracy for immediate response decisions while reducing overall processing time, as not all analysis steps need to complete before taking protective action
Data Source
AI summary
As described herein, a system, method, and computer program provide a computer attack response service. In use, a notification is received that a transfer of at least one electronic file to a computing device has been detected as a potential incoming threat to the computing device. Responsive to the receiving the notification, at least one honeypot is created. Additionally, data within the at least one electronic file is accessed, using the at least one honeypot. Responsive to accessing the data within the at least one electronic file, activity associated with the incoming threat is monitored.