Micron technology, inc. (20240160738). Detection of Anomalous Sequences of Commands to Memory Systems simplified abstract
Contents
- 1 Detection of Anomalous Sequences of Commands to Memory Systems
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Detection of Anomalous Sequences of Commands to Memory Systems - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Detection of Anomalous Sequences of Commands to Memory Systems
Organization Name
Inventor(s)
Saideep Tiku of Folsom CA (US)
Detection of Anomalous Sequences of Commands to Memory Systems - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160738 titled 'Detection of Anomalous Sequences of Commands to Memory Systems
Simplified Explanation
The abstract describes a device designed to detect attacks on a memory system in an advanced driver-assistance system (ADAS) of a vehicle. The device includes an interface operable on a memory channel, a random access memory, a non-volatile memory cell array, and a controller. The controller is configured to detect trigger events, identify a sequence of commands received in the interface from the memory channel to access memory services provided via at least the random access memory during ADAS operations, perform operations of multiplication and accumulation using the non-volatile memory cell array to implement computations of an artificial neural network responsive to the sequence of commands as an input to generate a classification of the sequence as an output, and provide the classification via the interface.
- The device is designed to detect attacks on a memory system in an advanced driver-assistance system (ADAS) of a vehicle.
- It includes an interface, random access memory, non-volatile memory cell array, and a controller.
- The controller can detect trigger events and identify sequences of commands to access memory services during ADAS operations.
- It performs operations using the non-volatile memory cell array to implement computations of an artificial neural network for classification.
- The classification is then provided via the interface.
Potential Applications
This technology could be applied in various industries beyond automotive, such as cybersecurity, data protection, and artificial intelligence.
Problems Solved
This device helps in detecting attacks on memory systems in ADAS, ensuring the safety and reliability of the system.
Benefits
The device enhances the security of ADAS systems, improves data protection, and enhances the overall performance of the vehicle's driver-assistance features.
Potential Commercial Applications
"Enhancing Memory System Security in ADAS Systems: Potential Commercial Applications"
Possible Prior Art
There may be prior art related to memory system security in ADAS systems, but specific examples are not provided in this context.
Unanswered Questions
How does this device compare to existing memory system security solutions in ADAS systems?
The article does not provide a direct comparison with existing solutions in the market.
What are the potential limitations or challenges of implementing this technology in ADAS systems?
The article does not address any potential limitations or challenges that may arise during the implementation of this technology.
Original Abstract Submitted
a device to detect attacks on a memory system in an advanced driver-assistance system (adas) of a vehicle. the device has an interface operable on a memory channel, a random access memory, a non-volatile memory cell array, and a controller configured to detect a trigger event, and in response: identify a sequence of commands received in the interface from the memory channel to access memory services provided via at least the random access memory during adas operations; perform operations of multiplication and accumulation using the non-volatile memory cell array to implement computations of an artificial neural network responsive to the sequence of commands as an input to generate a classification of the sequence as an output; and provide the classification via the interface.
