The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos ...The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corre- sponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.展开更多
Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor networ...Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.展开更多
The analytic criteria for the local activity theory in one-port cellularneural network (CNN) with five local state variables are presented. The application to a Hyper-chaossynchronization Chua's circuit (HCSCC) CN...The analytic criteria for the local activity theory in one-port cellularneural network (CNN) with five local state variables are presented. The application to a Hyper-chaossynchronization Chua's circuit (HCSCC) CNN with 1125 variables is studied. The bifurcation diagramsof the HCSCC CNN show that they are slightly different from the smoothed CNN with one or two portsand four state variables calculated earlier. The evolution of the patterns of the state variables ofthe HCSCC CNN is stimulated. Oscillatory patterns, chaotic patterns, convergent or divergentpatterns may emerge if the selected cell parameters are located in the locally active domains butnearby or in the edge of chaos domain.展开更多
超文本传输协议(Hyper Text Transfer Protocol,HTTP)隧道具有穿越防火墙和规避入侵检测系统识别的能力,给信息安全带来严重威胁。然而现阶段的HTTP隧道检测方法识别能力不足、难以应对特征复杂的HTTP隧道。文中分析了HTTP隧道数据包与...超文本传输协议(Hyper Text Transfer Protocol,HTTP)隧道具有穿越防火墙和规避入侵检测系统识别的能力,给信息安全带来严重威胁。然而现阶段的HTTP隧道检测方法识别能力不足、难以应对特征复杂的HTTP隧道。文中分析了HTTP隧道数据包与正常HTTP数据包之间的差别,针对目前HTTP隧道检测方法存在的不足,提出了一种仅需提取小部分流量数据的基于卷积神经网络的HTTP隧道检测方法。实验结果表明,基于卷积神经网络的HTTP隧道检测方法能有效识别网络中的HTTP隧道流量,检测精确率、召回率、F1分数均达到99%以上,且不需要人工选择大量的专家特征,对网络流量监管有重要意义。展开更多
基金supported by Key Program of Natural Science Fund of Tianjin of China (Grant No 07JCZDJC06600)
文摘The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corre- sponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.
基金supported by the National Natural Science Foundation of China(61602181,61876025)Program for Guangdong Introducing Innovative and Entrepreneurial Teams(2017ZT07X183)+2 种基金Guangdong Natural Science Foundation Research Team(2018B030312003)the Guangdong–Hong Kong Joint Innovation Platform(2018B050502006)the Fundamental Research Funds for the Central Universities(D2191200)
文摘Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.
基金the National Natural Science Foundation of China (Grant No. 60074034) and the Foundation forUniversity Key Teacher by the Ministry of Education of China.
文摘The analytic criteria for the local activity theory in one-port cellularneural network (CNN) with five local state variables are presented. The application to a Hyper-chaossynchronization Chua's circuit (HCSCC) CNN with 1125 variables is studied. The bifurcation diagramsof the HCSCC CNN show that they are slightly different from the smoothed CNN with one or two portsand four state variables calculated earlier. The evolution of the patterns of the state variables ofthe HCSCC CNN is stimulated. Oscillatory patterns, chaotic patterns, convergent or divergentpatterns may emerge if the selected cell parameters are located in the locally active domains butnearby or in the edge of chaos domain.
文摘超文本传输协议(Hyper Text Transfer Protocol,HTTP)隧道具有穿越防火墙和规避入侵检测系统识别的能力,给信息安全带来严重威胁。然而现阶段的HTTP隧道检测方法识别能力不足、难以应对特征复杂的HTTP隧道。文中分析了HTTP隧道数据包与正常HTTP数据包之间的差别,针对目前HTTP隧道检测方法存在的不足,提出了一种仅需提取小部分流量数据的基于卷积神经网络的HTTP隧道检测方法。实验结果表明,基于卷积神经网络的HTTP隧道检测方法能有效识别网络中的HTTP隧道流量,检测精确率、召回率、F1分数均达到99%以上,且不需要人工选择大量的专家特征,对网络流量监管有重要意义。
