Multi-Cloud Threat Simulation Using Live Data and KNN
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cybersecurity measures for multi-cloud environments are reactive and fail to anticipate and simulate real-time cyber threats effectively, leaving organizations vulnerable to sophisticated attacks, and existing machine learning solutions focus on post-incident analysis rather than proactive risk mitigation.
Innovation Solution
A proactive cyber threat simulator utilizing the K-nearest neighbors (KNN) algorithm for real-time threat simulation and prediction, integrating with existing frameworks to analyze and simulate potential threats within multi-cloud environments, continuously refining its model through adaptive learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cybersecurity measures are used, then the system can operate with simple reactive detection, but the system fails to anticipate and simulate real-time cyber threats effectively
Solution Approach 1:
The patent implements preliminary action by using the KNN algorithm to simulate potential cyber threats before they occur in real multi-cloud environments. The system creates virtual threat scenarios based on historical data and patterns, allowing organizations to proactively identify and mitigate risks before actual attacks happen, thereby improving threat detection effectiveness without requiring full-scale complex simulation infrastructure.
Solution Approach 2:
The patent applies the copying principle by creating simplified virtual representations of cyber threats and attack patterns rather than implementing full-scale complex simulation systems. The KNN algorithm generates synthetic threat data that mirrors real attack behaviors, enabling effective threat anticipation and simulation using lighter computational resources and simpler system architecture.
2Ease of operation
If reactive cybersecurity measures are implemented, then the system maintains simplicity in operation, but decision-making is hampered due to inability to predict threats
Solution Approach 1:
The system performs preliminary threat simulations and generates predictive insights before actual security incidents occur. By pre-analyzing potential threat scenarios using the KNN algorithm and historical data, the system prepares actionable intelligence in advance, enabling faster decision-making when real threats emerge without complicating the operational interface for security personnel.
3Use of energy by moving object
If traditional threat detection methods are used, then the system requires minimal computational resources, but the system cannot effectively simulate and adapt to emerging cyber threats
Solution Approach 1:
The patent uses copying by creating simplified virtual models of complex cyber threats through the KNN algorithm. Instead of requiring massive computational resources to simulate every possible attack vector, the system generates representative synthetic threat data that captures essential attack patterns and behaviors, achieving effective threat simulation and adaptability to emerging threats with moderate computational resource consumption.
Data Source
AI summary
Techniques described herein relate to a method for performing threat simulations. The method includes preparing a multi-cloud infrastructure (MCI) for live data collection, wherein: the MCI includes components, a first component of the components is associated with a first cloud service provider, and a second component of the components is associated with a second cloud service provider; obtaining, after the preparing, live data from the MCI; generating a prediction using the live data and a prediction model, wherein the prediction specifies whether the live data indicates that there is a threat within the MCI; making a first determination that the prediction indicates a threat associated with the MCI has been identified; and in response to the first determination: performing threat remediation using the prediction and a diagnostic repository.