18095391. MALWARE DETECTION ON ENCRYPTED DATA simplified abstract (Rubrik, Inc.)

MALWARE DETECTION ON ENCRYPTED DATA

Organization Name

Rubrik, Inc.

Inventor(s)

Magesh Kumar Mariappan of Bangalore (IN)

Shourya Jaiswal of Bangalore (IN)

Kimberly Myles of Bonita Springs FL (US)

MALWARE DETECTION ON ENCRYPTED DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18095391 titled 'MALWARE DETECTION ON ENCRYPTED DATA

Simplified Explanation

The patent application describes methods, systems, and devices for data management, specifically focusing on detecting malware on encrypted data associated with a computing system. The system uses machine learning models to analyze encrypted data and determine the presence of malware.

Key Features and Innovation

• Data management system supports malware detection on encrypted data.
• System instructs computing system to generate and encrypt machine learning model features.
• Receives encrypted machine learning model features from computing system.
• Uses machine learning model to analyze encrypted features and determine malware presence.
• Transmits encrypted indication of malware presence back to computing system.

Potential Applications

This technology can be applied in various industries where data security is crucial, such as cybersecurity, financial services, healthcare, and government sectors.

Problems Solved

The technology addresses the challenge of detecting malware on encrypted data, providing an additional layer of security for computing systems.

Benefits

• Enhanced data security through malware detection on encrypted data.
• Improved threat detection capabilities for computing systems.
• Efficient use of machine learning models for analyzing encrypted data.

Commercial Applications

• Cybersecurity companies can integrate this technology into their products to enhance malware detection capabilities.
• Financial institutions can use this system to protect sensitive financial data from cyber threats.

Prior Art

Readers can explore prior research on malware detection in encrypted data and machine learning applications in cybersecurity to understand the background of this technology.

Frequently Updated Research

Stay updated on advancements in machine learning algorithms for malware detection and encryption techniques for secure data management.

Questions about Data Management System

How does the system ensure the privacy of encrypted data during malware detection?

The system uses encryption techniques to protect the privacy of data while analyzing it for malware, ensuring that sensitive information remains secure.

What are the potential limitations of using machine learning models for malware detection on encrypted data?

One potential limitation could be the computational resources required to process large amounts of encrypted data using machine learning models.

Original Abstract Submitted

Methods, systems, and devices for data management are described. A data management system (DMS) may support the detection of malware on encrypted data associated with a computing system that is backed up by the DMS. For example, the DMS may transmit first signaling that instructs the computing system to generate and encrypt one or more machine learning (ML) model features associated with a computing object of the computing system. In response, the DMS may receive second signaling from the computing system that includes the one or more encrypted ML model features. The DMS may use an ML model and the one or more encrypted ML model features as inputs to the ML model to generate and transmit, to the computing system, an encrypted indication of whether malware is present on the computing object.