Anomaly Detection for Backup Recovery Prioritization

The system performs automated analysis of backup data to identify infection patterns and prioritize recovery nodes without requiring manual operator intervention. The computer-implemented method automatically scans backup copies, detects anomalies indicating malware presence, and generates prioritization recommendations, allowing the system to serve itself in the recovery decision-making process.

The patent replaces manual mechanical analysis processes with automated computer-based analysis. Instead of operators manually examining backup data and making prioritization decisions, the system uses automated scanning, pattern recognition, and anomaly detection algorithms to perform these functions, substituting human cognitive and manual labor with computational processes.

The system performs scanning of backup copies in a prioritized sequence based on infection likelihood, rather than scanning all copies equally. It focuses computational resources on the most suspicious backups first, performing partial scanning initially and only expanding to comprehensive scanning of high-priority targets if needed, thus achieving good detection accuracy without processing every single backup copy to full depth.

The patent applies different scanning intensities and methods to different backup copies based on their individual risk profiles. High-priority backups suspected of containing malware receive more thorough scanning, while lower-priority backups receive lighter scanning or are skipped entirely, allowing the system to allocate detection resources locally where they are most needed rather than uniformly across all backups.

The system performs preliminary scanning and verification of backup copies before restoration occurs. By detecting malware and classifying backups as anomalous or clean in advance, the system ensures that only verified clean backups are selected for restoration, eliminating the need for post-restoration cleaning and preventing malware reintroduction while maintaining efficient recovery speeds.

The patent implements preliminary anti-action by proactively detecting and flagging infected backup copies before they can be restored. The anomaly detection and pattern recognition processes identify malware presence in advance, allowing the system to prevent restoration of contaminated backups and counteract the potential harmful effect of malware reintroduction before it can occur.

The system uses pattern matching that can be replicated and applied across multiple machines using the same trained algorithms. Once the anomaly detection patterns are learned from initial data, they can be copied and applied uniformly across the entire fleet of machines, achieving consistent prioritization accuracy without requiring complex custom analysis for each individual machine.

The patent implements a universal pattern matching system that serves multiple functions: detecting anomalies in backup data, prioritizing recovery nodes, and identifying infection patterns across different machines. The same core algorithm and pattern recognition engine handle all these tasks, reducing overall system complexity by using a single multi-functional approach rather than separate specialized systems for each function.