Lina Yu

In this paper, we focus on the robust fixed-time synchronization for discontinuous neural networks (NNs) with delays and hybrid couplings under uncertain disturbances, where the growth of discontinuous activation functions is governed by a quadratic polynomial. New state-feedback controllers, which include integral terms and discontinuous factors, are designed. By Lyapunov–Krasovskii functional method and inequality analysis technique, some sufficient criteria, which ensue that networks can realize the robust fixed-time synchronization, are addressed in terms of linear matrix inequalities (LMIs). Moreover, the upper bound of the settling time, which is independent on the initial values, can be determined to any desired values in advance by the configuration of parameters in the proposed control law. Finally, two examples are provided to illustrate the validity of the theoretical results.

This paper is concerned with the global finite-time and fixed-time synchronization for a class of discontinuous complex dynamical networks with semi-Markovian switching and mixed time-varying delays. The novel state-feedback controllers, which include integral terms and discontinuous facts, are designed to realize the global synchronization between the drive system and response system. By applying the Lyapunov functional method and matrix inequality analysis technique, the global finite-time and fixed-time synchronization conditions are addressed in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the feasibility of the proposed control scheme and the validity of theoretical results.

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN (K-Nearest Neighbors), DT (Decision Tree), and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises.

A DLP (data loss prevention) system usually arranges network monitors at the network boundary to perform network traffic capture, file parsing, and strategy matching procedures. Strategy matching is a key process to prevent corporate secret-related documents from leaking. This paper adopts the document fingerprint similarity detection method based on the SimHash principle and customizes the KbS (Keyword-based SimHash) fingerprint, PbS (Paragraph-based SimHash) fingerprint, and SoP (SimHash of Paragraph) fingerprint, three different feature extraction SimHash algorithms for strategy matching to detect. The parsed unstructured data is stored as a file type in.txt format, and then a file fingerprint is generated. Matching the established sensitive document library to calculate the Hamming distance between the fingerprints, the Hamming distance values under different modification degrees are summarized. The experimental results reveal that the hybrid algorithmic strategy matching rules with different levels and accuracy are established. This paper has a reference role for the leakage prevention research of enterprise sensitive data.

Welcome to the WIT Press eLibrary - the home of the Transactions of the Wessex Institute collection, providing on-line access to papers presented at the Institute's prestigious international conferences and from its State-of-the-Art in Science & Engineering publications.