Google llc (20240134980). IDENTIFY MALICIOUS SOFTWARE simplified abstract
Contents
- 1 IDENTIFY MALICIOUS SOFTWARE
IDENTIFY MALICIOUS SOFTWARE
Organization Name
Inventor(s)
Richard Cannings of Santa Cruz CA (US)
Sai Deep Tetali of Mountain View CA (US)
Mo Yu of Mountain View CA (US)
Salvador Mandujano of San Jose CA (US)
IDENTIFY MALICIOUS SOFTWARE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134980 titled 'IDENTIFY MALICIOUS SOFTWARE
Simplified Explanation
The abstract describes a method for identifying malicious software using a feed-forward neural network to analyze the interactions of a software application with various uniform resource identifiers.
- Receiving and executing a software application
- Identifying a plurality of uniform resource identifiers the software application interacts with
- Generating a vector representation for the software application using a feed-forward neural network
- Determining similarity scores with a pool of training applications
- Flagging the software application as potentially harmful if similarity scores meet a threshold
Potential Applications
This technology can be applied in cybersecurity to detect and prevent the spread of malicious software, protecting systems and networks from potential threats.
Problems Solved
This technology addresses the challenge of identifying and categorizing potentially harmful software applications that may pose a risk to computer systems and data security.
Benefits
- Enhanced security measures against malicious software - Efficient identification and categorization of potentially harmful applications - Improved protection of systems and networks from cyber threats
Potential Commercial Applications
The technology can be utilized by cybersecurity companies, software developers, and organizations looking to enhance their security measures and protect their systems from malware attacks.
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms to detect malware and classify software applications based on their behavior and characteristics.
Unanswered Questions
How does this method compare to traditional antivirus software in terms of effectiveness and efficiency?
This article does not provide a direct comparison between this method and traditional antivirus software in terms of their effectiveness in identifying and preventing malicious software.
What are the limitations of using a feed-forward neural network for identifying malicious software?
The article does not discuss any potential limitations or challenges associated with using a feed-forward neural network for identifying malicious software.
Original Abstract Submitted
a method for identifying malicious software includes receiving and executing a software application, identifying a plurality of uniform resource identifiers the software application interacts with during execution of the software application, and generating a vector representation for the software application using a feed-forward neural network configured to receive the plurality of uniform resource identifiers as feature inputs. the method also includes determining similarity scores for a pool of training applications, each similarity score associated with a corresponding training application and indicating a level of similarity between the vector representation for the software application and a respective vector representation for the corresponding training application. the method also includes flagging the software application as belonging to a potentially harmful application category when one or more of the training applications have similarity scores that satisfy a similarity threshold and include a potentially harmful application label.
