Verifying security protocols by knowledge analysis

This paper describes a new interactive method to analyse knowledge of participants involved in security protocols and further to verify the correctness of the protocols. The method can detect attacks and flaws involving interleaving sessions besides normal attacks. The implementation of the method in a generic theorem proving environment, namely Isabelle, makes the verification of protocols mechanical and efficient; it can verify a medium-sized security protocol in less than ten seconds. As an example, the paper finds the flaw in the Needham-Schroeder public key authentication protocol and proves the secure properties and guarantees of the protocol with Lowe's fix to show the effectiveness of this method

Enhanced sampling in generalized ensemble with large gap of sampling parameter: case study in temperature space random walk

We present an efficient sampling method for computing a partition function and accelerating configuration sampling. The method performs a random walk in the $\lambda$ space, with $\lambda$ being any thermodynamic variable that characterizes a canonical ensemble such as the reciprocal temperature $\beta$ or any variable that the Hamiltonian explicitly depends on. The partition function is determined by minimizing the difference of the thermal conjugates of $\lambda$ (the energy in the case of $\lambda=\beta$), defined as the difference between the value from the dynamically updated derivatives of the partition function and the value directly measured from simulation. Higher-order derivatives of the partition function are included to enhance the Brownian motion in the $\lambda$ space. The method is much less sensitive to the system size, and the size of $\lambda$ window than other methods. On the two dimensional Ising model, it is shown that the method asymptotically converges the partition function, and the error of the logarithm of the partition function is much smaller than the algorithm using the Wang-Landau recursive scheme. The method is also applied to off-lattice model proteins, the $AB$ models, in which cases many low energy states are found in different models.Comment: 7 pages, 3 figure

The Effects of Financial Crises on International Trade

This paper studies empirically the effects of financial crises on international trade. The major findings are that banking crises had a negative impact on imports but a positive impact on exports in the short term, whereas currency crises decreased imports in the short term and stimulated exports in the longer term.

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores