AI Threat Anticipation via Evolutionary Randomization
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Solution Overview
Problem
Current cybersecurity measures are inadequate for anticipating and proactively addressing malware threats, relying on detection and reaction rather than anticipation, which is insufficient against the rapidly evolving cyber threats, resulting in delayed responses measured in months rather than seconds.
Innovation Solution
A computer-implemented system that includes an evolution engine to randomize and evaluate malware threats, using algorithms such as bit extraction and manipulation, signature reordering, and generative artificial intelligence to generate new threat signatures, and an evaluation engine that uses Bayesian probability analysis for proactive detection and continuous updating of threat data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection and reaction methods are used, then the system can identify known malware threats, but the response time is delayed measured in months rather than seconds
Solution Approach 1:
The system performs preliminary actions by generating and evaluating randomized malware threats before they actually occur in the wild. The evolution engine continuously creates hypothetical threat scenarios and the evaluation engine assesses them in advance, so when a real threat materializes, the system has already prepared defensive measures, reducing response time from months to seconds.
Solution Approach 2:
The system creates copies of existing malware threats by randomizing and evolving known threat patterns. Instead of waiting to detect actual malware, the system generates synthetic copies through the evolution engine, evaluates them proactively, and builds defensive signatures based on these copied and modified threat patterns.
2Reliability
If the number of Red Team scenarios is increased to improve threat anticipation, then the capability to predict threats improves, but the time required to explore all scenarios becomes impractical
Solution Approach 1:
The system applies dynamics by using evolutionary algorithms that continuously adapt and transform threat scenarios. Rather than statically analyzing a fixed large number of scenarios, the evolution engine dynamically generates new threat variations through randomization and mutation, allowing the system to explore threat space efficiently by focusing on the most promising evolutionary paths.
Solution Approach 2:
The system serves itself by automatically generating and evaluating threat scenarios without requiring extensive human Red Team intervention. The evolution engine autonomously creates randomized threats and the evaluation engine automatically assesses them, enabling the system to process vast numbers of scenarios at machine speed without human time constraints.
3Ease of manufacture
If conventional antivirus and antispyware tools are used, then the implementation is straightforward and well-documented, but these tools are easily overcome by threat developers
Solution Approach 1:
The system changes parameters by transforming static malware signatures into dynamic, evolving threat models. Instead of relying on fixed conventional antivirus signatures that threat developers can easily bypass, the system continuously modifies threat parameters through randomization and evolution, creating adaptive defense mechanisms that remain effective against new and emerging threats.
Data Source
AI summary
According to various embodiments, a system for, and method of, predicting and remediating malware threats in an electronic computer network, is provided. The disclosed techniques include storing in an electronic persistent storage library data representing a plurality of malware threats, randomizing, by a computer-implemented evolution engine communicatively coupled to the electronic persistent storage library, data representing malware threats to generate data representing randomized malware threats, and evaluating, by a computer-implemented evaluation engine communicatively coupled to an output of the evolution engine and to the electronic persistent storage library, the data representing the randomized malware threats, where the evaluation engine adds data representing positively evaluated randomized malware threats to the library for proactive detection of future malware threats in the electronic computer network.