Biblio

Polymerization process is a process in the production of polyester fiber, and its reaction parameter intrinsic viscosity has an important influence on the properties of the final polyester fiber. In this paper, a feature selection extreme learning machine model based on binary encoding Atom Search Optimization algorithm is proposed and applied to the polymerization process of polyester fiber production. Firstly, the distance measure of K-NearestNeighbor algorithm, combined with binary coding, and Atom Search Optimization algorithm are used to select features of industrial data to obtain the optimal data set. According to the data set, atom search optimization algorithm is used to optimize the weight and threshold of extreme learning machine and the activation function of the improved extreme learning machine. A prediction model with root mean square error as fitness function was established and applied to polyester production process. The simulation results show that the model has good prediction accuracy, which can be used for reference in the follow-up industrial production.

ID/password-based authentication is commonly used in network services. Some users set different ID/password pairs for different services, but other users reuse a pair of ID/password to other services. Such recycling allows the list attack in which an adversary tries to spoof a target user by using a list of IDs and passwords obtained from other system by some means (an insider attack, malwares, or even a DB leakage). As a countermeasure agains the list attack, biometric authentication attracts much attention than before. In 2012, Hattori et al. proposed a cancelable biometrics authentication scheme (fundamental scheme) based on homomorphic encryption algorithms. In the scheme, registered biometric information (template) and biometric information to compare are encrypted, and the similarity between these biometric information is computed with keeping encrypted. Only the privileged entity (a decryption center), who has a corresponding decryption key, can obtain the similarity by decrypting the encrypted similarity and judge whether they are same or not. Then, Hirano et al. showed the replay attack against this scheme, and, proposed two enhanced authentication schemes. In this paper, we propose a spoofing attack against the fundamental scheme when the feature vector, which is obtained by digitalizing the analogue biometric information, is represented as a binary coding such as Iris Code and Competitive Code. The proposed attack uses an unexpected vector as input, whose distance to all possible binary vectors is constant. Since the proposed attack is independent from the replay attack, the attack is also applicable to two revised schemes by Hirano et al. as well. Moreover, this paper also discusses possible countermeasures to the proposed spoofing attack. In fact, this paper proposes a countermeasure by detecting such unexpected vector.