Parallel Malware Scanning Across Cloud Agents
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Solution Overview
Problem
Current malware scanning in cloud-connected devices is cumbersome and limited by processing power, requiring manual disinfection across multiple devices even after detection, and lacks efficient parallel scanning capabilities.
Innovation Solution
Implementing a system that synchronizes cloud content across multiple agents, allowing parallel scanning and aggregation of results to identify and remove malware from all connected devices, leveraging checksums and push notification channels to distribute scanning workload efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If antivirus software scans cloud content on a single device, then the scanning operation is limited by the processing power of that device, but implementing parallel scanning across multiple agents increases system complexity
Solution Approach 1:
The patent divides the cloud content into multiple portions and assigns each portion to different agents for parallel scanning. The server coordinates the segmentation and distribution of scanning tasks across multiple devices, allowing the system to leverage combined processing power while maintaining manageable complexity through centralized coordination.
Solution Approach 2:
The patent combines multiple agents into a coordinated scanning network where each agent contributes its processing power. The server merges the scanning results from all agents to produce a comprehensive security assessment, effectively combining individual device capabilities into a unified high-power scanning system.
2Reliability
If users manually scan and disinfect infected files from cloud storage and all connected devices, then comprehensive malware removal is achieved, but the process is cumbersome and time-consuming
Solution Approach 1:
The system implements automated malware detection and response where the scanning agents autonomously identify infected files and the server automatically coordinates disinfection actions across all devices. This eliminates the need for users to manually scan and disinfect files on each device, as the system performs these tasks automatically while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements a feedback mechanism where scanning results are reported back to the server, which then automatically triggers disinfection actions. The system continuously monitors and responds to malware threats through automated feedback loops, ensuring comprehensive removal without requiring user intervention.
3Productivity
If cloud content is pre-downloaded to a laptop for scanning, then scanning can be performed locally, but the pre-downloading process is cumbersome and defeats the purpose of cloud storage
Solution Approach 1:
The patent makes the cloud storage system itself multi-functional by enabling it to perform both data storage and malware scanning operations. The scanning agents operate directly on cloud-hosted content without requiring downloads, allowing the system to maintain its cloud-based nature while adding scanning capability.
Solution Approach 2:
The patent introduces scanning agents as intermediaries that operate between the user's local device and the cloud storage. These agents enable local-like scanning performance by processing content in the cloud while maintaining user convenience, as no data download is required and the interface remains simple.
4Device complexity
If a single agent scans all synchronized data, then the scanning process is simple to coordinate, but the scanning time increases significantly with large data volumes
Solution Approach 1:
The patent segments the scanning task by dividing synchronized data into portions and assigning them to multiple agents simultaneously. This parallel processing approach dramatically reduces scanning time for large data volumes while the server maintains coordination simplicity through centralized task distribution and result aggregation.
Data Source
AI summary
Disclosed herein are systems and methods for parallel malware scanning in a cloud environment. In one exemplary aspect, a method may comprise identifying a plurality of agents connected to a server, wherein each agent is configured to synchronize data between a different computing device and the server. The method may comprise receiving, from a first agent of the plurality of agents, a request to scan the synchronized data for malware. In response to determining, from the plurality of agents, at least one other agent that comprises the synchronized data, the method may comprise partitioning the synchronized data into a plurality of portions. The method may comprise assigning a first portion for scanning to the first agent and at least one other portion for scanning to the at least one other agent, and aggregating scan results from the first agent and the at least one other agent.