Memory Device User Behavior Learning Module
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing memory devices lack effective protection mechanisms against malicious users, as they cannot distinguish between normal and anomalous user behavior, leading to potential data access by unauthorized individuals.
Innovation Solution
Incorporating a user behavior learning module within the memory device's firmware to monitor and learn user patterns, detecting anomalies and implementing a step-up locking mechanism to prevent unauthorized access, such as timed, staggered, or total locks, based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional memory devices are used without behavior monitoring, then device complexity is low and ease of operation is maintained, but security protection against malicious users is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the memory device continuously monitors user operations and compares them against learned behavior patterns. When anomalous operations are detected, the system responds by applying locking mechanisms. This closed-loop feedback system enables the device to adaptively protect data based on real-time operation analysis, resolving the contradiction between maintaining low device complexity and achieving high data security.
Solution Approach 2:
The memory device performs self-monitoring and self-protection by autonomously analyzing user operations and applying security measures without requiring external intervention. The device learns user behavior patterns and independently determines when to apply locking mechanisms, enabling it to serve its own security needs and resolve the contradiction between simplicity and security.
2Reliability
If a user behavior learning module is added to monitor and detect anomalies, then data security is improved, but device complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary learning of user behavior patterns during normal operation phases, building a baseline of acceptable operations before security threats occur. This preliminary action enables the device to quickly recognize anomalies without requiring complex real-time analysis during critical security moments, thereby improving data security while managing firmware complexity.
Solution Approach 2:
The security mechanism dynamically adjusts its monitoring and response behavior based on the learned user patterns. The system transitions between different operational states (normal monitoring, anomaly detection, locking) based on real-time operation analysis, allowing it to maintain high security without permanently operating at maximum complexity levels.
3Reliability
If step-up locking mechanisms are applied based on anomaly detection, then protection against malicious access is enhanced, but user accessibility and ease of operation may be reduced
Solution Approach 1:
The locking mechanism applies different levels of security to different operational contexts. Instead of uniformly locking all operations, the system applies locking only to specific operations that deviate from learned user patterns. This localized application of security measures maintains ease of operation for normal user activities while providing strong protection against malicious access attempts.
Solution Approach 2:
The security system dynamically adjusts the locking level based on the severity and type of detected anomalies. The step-up locking mechanism transitions from no lock to partial lock to full lock based on the confidence level of anomaly detection, allowing normal operations to proceed smoothly while progressively increasing protection as threats are identified.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides an additional layer of security by dynamically adapting to user behavior, enhancing protection against malicious access without compromising performance, even on legacy devices, and allowing for recovery mechanisms to prevent complete lockdowns.
Implementation Method 1
applying a high positive voltage, which may be referred to as a 'program voltage,' a 'programming power voltage,' or 'PPV,' to a control gate to generate Fowler-Nordheim tunneling (referred to as 'F—N tunneling') between a floating gate and the semiconductor substrate. When F—N tunneling is occurring, electrons of the bulk area are accumulated on the floating gate by an electric field of VPP applied to the control gate to increase a threshold voltage of the memory cell.
Implementation Method 2
An erasing operation of the memory cell is concurrently performed in units of sectors sharing the bulk area (referred to as 'blocks'), by applying a high negative voltage, which may be referred to as an 'erase voltage' or 'Vera,' to the control gate and a configured voltage to the bulk area to generate the F—N tunneling. In this case, electrons accumulated on the floating gate are discharged into the source area, so that the memory cells have an erasing threshold voltage distribution.
Data Source
AI summary
In some implementations, a memory device may include one or more components. The one or more components may be configured to identify an operation to access content stored in a memory of the memory device, wherein the operation is associated with a user profile. The one or more components may be configured to flag a user, associated with the user profile, as being potentially malicious based on the operation conflicting with a past content access pattern associated with the user profile. The one or more components may be configured to lock the memory based on the user being flagged.