Hanping Hu

Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization

This paper presents a hybrid differential evolution (DE) with quantum-behaved particle swarm optimization (QPSO) for the unmanned aerial vehicle (UAV) route planning on the sea. The proposed method, denoted as DEQPSO, combines the DE algorithm with the QPSO algorithm in an attempt to further enhance the performance of both algorithms. The route planning for UAV on the sea is formulated as an optimization problem. A simple method of pretreatment to the terrain environment is proposed. A novel route planner for UAV is designed to generate a safe and flyable path in the presence of different threat environments based on the DEQPSO algorithm. To show the high performance of the proposed method, the DEQPSO algorithm is compared with the real-valued genetic algorithm, DE, standard particle swarm optimization (PSO), hybrid particle swarm with differential evolution operator, and QPSO in terms of the solution quality, robustness, and the convergence property. Experimental results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization algorithms.

Pseudorandom sequence generator based on the Chen chaotic system

Abstract In this paper, the Chen chaotic system is proposed as a pseudorandom sequence generator. A new algorithm is created to solve the problem of non-uniform distribution of the sequence generated by the Chen chaotic system. Statistical tests and security analysis show that it has good pseudorandom characteristics and is highly capable of withstanding attacks.

Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications

Cloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been presented, which analyze dynamic characteristics of the network traffic based on the synergetic neural networks and the catastrophe theory. In the former approach, a synergetic dynamic equation with a group of the order parameters is used to describe the complex behaviors of the network traffic system in cloud communications. When this equation is evolved, only the order parameter determined by the primary factors can converge to 1. Then, the anomaly can be detected. In the latter approach, a catastrophe potential function is introduced to describe the catastrophe dynamic process of the network traffic in cloud communications. When anomalies occur, the state of the network traffic will deviate from the normal one. To assess the deviation, an index named as catastrophe distance is defined. The network traffic anomaly can be detected by the value of this index. We evaluate the performance of these two approaches using the standard Defense Advanced Research Projects Agency data sets. Experimental results show that our approaches can effectively detect the network traffic anomaly and achieve the high detection probability and the low false alarms rate.

A general hybrid model for chaos robust synchronization and degradation reduction

This paper focuses on the problem of robust synchronization of uncertain continuous chaos and dynamical degradation of digital chaos. A hybrid model is established based on the complementarities between continuous chaos and digital chaos. An impulse-like controller along with a state feedback controller is designed to guarantee the robust synchronization of uncertain continuous chaotic systems and reduce the dynamical degradation of digital chaotic systems respectively. Simulation studies are conducted to illustrate the effectiveness of the hybrid model. Compared with the existing synchronization schemes, this model can realize the synchronization of two uncertain continuous chaotic systems without transmission of synchronization control signals and has better robustness. Meanwhile, it can make the properties of given digital chaotic systems achieve desirable levels, while the existing remedies for digital chaotic systems fail to. Thus, the hybrid model is very applicable to cryptography, secure communication and other potential applications.

Analysis and Design of Digital Chaotic Systems With Desirable Performance via Feedback Control

The dynamical degradation of digital chaotic systems (DCSs) often has serious negative influences on some digital chaos-based systems and then becomes one of the bottleneck problems stopping chaos from some applications. In this paper, we first restrict the Devaneys chaos definition to finite state sets to describe the chaotic motion of digital systems. Then, we propose a novel control method for DCSs based on the differential mean value theorem and state feedback technology. Simulation results show the effectiveness, robustness, and superiority of the proposed method. Finally, we construct a new pseudorandom number generator (PRNG) and evaluate its randomness via NIST SP800-22 and TestU01 test suites. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications.

Robust Synchronization by Time-Varying Impulsive Control

This brief introduces a new impulsive synchronization scheme for a class of nonlinear systems with time-varying impulsive interval. Different from existing fixed time synchronization schemes, the impulsive synchronization intervals in our scheme are adaptive to the states of the driving system. Compared with existing schemes, the average impulsive interval can be enlarged significantly. Thus, the requirement of bandwidth for the transmission of impulsive signal is also reduced.

Anomaly detection of network traffic based on the largest Lyapunov exponent

A Real-time and reliable detection of anomalies is an important and challenging task. Unlike most detection methods based on the statistical analysis of the packet headers (Such as IP addresses and ports), we propose a new nonlinear approach only using network traffic volumes to detect anomalies reliably. Our method is based on the largest Lyapunov exponent and the change-point detection theory to judge whether anomalies have happened. In details, the largest Lyapunov exponents of normal and anomaly data fluctuate slightly respectively while those of the overlapped data composed of them fluctuate greatly because the dynamic structure of data has changed. Experimental results on network traffic volumes transformed from 1999 DARPA intrusion evaluation data set show that this method can more effectively detect network anomalies contrast to a linear method.1

Image watermarking technique based on two-dimensional chaotic stream encryption

This paper proposes a kind of wavelet domain image digital watermarking technique using two-dimensional chaotic stream encryption and human visual model. A stream encryption algorithm based on two-dimensional Logistic chaotic map is researched and realized for meaningful grayscale watermarking image. The block embedding intensity is calculated and combined with the human visual model, so that the embedding and detection steps of encrypted binary watermark can be adaptively fulfilled in the wavelet coefficients of the host image. The experimental results have shown that this watermarking technique can endure regular digital image processing and have preferable performance.

Multiscale Entropy Analysis on Human Operating Behavior

By exploiting the statistical analysis method, human dynamics provides new insights to the research of human behavior. In this paper, we analyze the characteristics of the computer operating behavior through a modified multiscale entropy algorithm with both the interval time series and the number series of individuals’ operating behavior been investigated. We also discuss the activity of individuals’ behavior from the three groups denoted as the retiree group, the student group and the worker group based on the nature of their jobs. We find that the operating behavior of the retiree group exhibits more complex dynamics than the other two groups and further present a reasonable explanation for this phenomenon. Our findings offer new insights for the further understanding of individual behavior at different time scales.

On the eigenvalue and Shannon's entropy of finite length random sequences

Pseudorandom binary sequences play a significant role in many fields, such as spread spectrum communications, stochastic computation, and cryptography. The complexity measures of sequences and their relationship still remain an interesting open problem. In this article, we study on the eigenvalue of random sequences, deduce its theoretical expectation and variance of random sequences with length N, and establish the relationship between eigenvalue and Shannons entropy. The results show that these two measures are consistent. Furthermore, the eigenvalue of random n-block sequences and its relation to Shannons entropy are also been studied.