Malware Identity Identification in Data Management Systems
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Solution Overview
Problem
Current data management systems face challenges in providing sufficient information for users to quickly identify the cause of anomalies detected in uploaded data, such as malware infections, leading to increased time and effort in investigation and remediation.
Innovation Solution
A data management system that extracts and augments features from uploaded data with malware identity information, comparing them to a malware signature repository to identify potential malware identities and indicate suspicious files, facilitating faster anomaly detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a data management system detects anomalies in uploaded data, then security is improved, but the system provides insufficient information about the cause of anomalies, increasing investigation time and effort
Solution Approach 1:
The system performs preliminary analysis by extracting features from uploaded data and comparing them against a malware signature repository before presenting results to users. This preliminary action of pre-processing and pre-comparing malware signatures allows the system to provide ready-made anomaly cause information, reducing the time users need to spend investigating anomalies without compromising security detection capability
2Measurement precision
If the system extracts and compares features with malware signatures, then malware identification capability is improved, but system complexity increases
Solution Approach 1:
The malware detection system is segmented into distinct functional modules: a feature extraction component that extracts relevant features from uploaded data, a malware signature repository that stores known malware patterns, and a comparison component that matches features against signatures. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while maintaining high identification accuracy
Solution Approach 2:
The system introduces an intermediary feature extraction layer between the uploaded data and the malware signature comparison. This intermediary component transforms raw data into standardized features that can be efficiently compared against malware signatures, reducing the complexity of direct comparison while improving identification accuracy
Data Source
AI summary
Techniques and configurations for data management are described. Features may be extracted from backup data stored in a data management system for a target object, where the backup data may reflect the target object at a point-in-time. An anomaly associated with the target object may be detected based on the features extracted from the backup data. Based on detecting the anomaly, a malware identity associated with the anomaly may be identified based on the features extracted from the backup data. The identified malware identity may be indicated via a user interface.