LSTM-Based Encryption Level Selection for Data Security
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Solution Overview
Problem
Current encryption methods fail to select an appropriate level of encryption based on the cognitive states of senders and receivers, leading to inadequate security and increased risk of unauthorized access to valuable data.
Innovation Solution
A method that determines the cognitive states of senders and receivers using various metrics, generates feature vectors, and inputs them into a long short-term memory network to select an appropriate encryption level, including algorithm, strength, and mode of transportation for securing data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption algorithm selection is based on user or system determination without cognitive state analysis, then the encryption process is simple, but the security level is insufficient and may be too weak to prevent unauthorized access
Solution Approach 1:
The system performs preliminary analysis of cognitive states of senders and receivers before selecting encryption algorithms. By measuring cognitive states in advance and using LSTM networks to predict appropriate encryption levels, the system prepares security measures proactively rather than reactively, ensuring adequate security before data transmission occurs.
Solution Approach 2:
The system continuously monitors cognitive states of users and feeds this information back to the encryption selection mechanism. The LSTM network processes this feedback to dynamically adjust encryption algorithm selection, creating a closed-loop system that adapts security levels based on real-time user cognitive states, thereby improving security without requiring overly complex manual configuration.
2Reliability
If encryption strength is increased to prevent unauthorized access, then security is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system dynamically adjusts encryption strength based on the cognitive states of senders and receivers. Rather than using fixed high-level encryption for all transmissions, the LSTM network predicts appropriate encryption levels that adapt to user states, computational requirements, and message importance. This dynamic approach ensures adequate security while optimizing computational resource utilization by avoiding unnecessary over-encryption.
3Reliability
If the encryption level is selected based on end-to-end communications without cognitive state measurement, then the implementation is straightforward, but the appropriate level of encryption may not be achieved making data more accessible to unauthorized users
Solution Approach 1:
The system introduces cognitive state measurement tools and LSTM networks as intermediaries between users and the encryption selection process. These intermediaries translate complex cognitive state data into actionable insights for encryption algorithm selection, bridging the gap between difficult-to-measure user states and practical security decisions without requiring direct manual intervention.
Solution Approach 2:
The system enables automated encryption level selection that serves itself by measuring cognitive states and making informed decisions without constant human intervention. The LSTM network learns from patterns in cognitive state data and automatically determines appropriate encryption levels, reducing the burden on users while improving security outcomes through data-driven decisions.
Data Source
AI summary
A method, system and computer program product for securing data transmission. After determining that a message is being created to be sent from a sender to a receiver, the cognitive states of the sender and receiver are measured. A set of feature vectors for the sender and receiver is generated based on the cognitive states of the sender and receiver. The feature vectors are inputted into a long short term memory network, which selects an appropriate level of encryption (encryption strength, encryption algorithm, and mode of transportation in sending the message) to encapsulate the message utilizing the generated set of feature vectors. The message is then encapsulated using the selected level of encryption. In this manner, by using an appropriate level of encryption, the message is less likely to be accessible by unauthorized users thereby reducing the likelihood of unauthorized access or loss of valuable data.