Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.
Embedded software developers assume the behavior of the environment when specifications are not available. However, developers may assume the behavior incorrectly, which may result in critical faults in the system. Therefore, it is important to detect the faults caused by incorrect assumptions. In this letter, we propose a log-based testing approach to detect the faults. First, we create a UML behavioral model to represent the assumed behavior of the environment, which is then transformed into a state model. Next, we extract the actual behavior of the environment from a log, which is then incorporated in the state model, resulting in a state model that represents both assumed and actual behaviors. Existing testing techniques based on the state model can be used to generate test cases from our state model to detect faults.
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