Jieren Cheng^1,2, Ruomeng Xu^1,*, Xiangyan Tang¹, Victor S. Sheng³, Canting Cai¹

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems. We define… More >

Yuling Liu^1,*, Hua Peng¹, Jie Wang²

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 37-57, 2018, DOI:10.3970/cmc.2018.055.037

Abstract Data outsourcing has become an important application of cloud computing. Driven by the growing security demands of data outsourcing applications, sensitive data have to be encrypted before outsourcing. Therefore, how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue. Searchable encryption scheme is proposed to allow users to search over encrypted data. However, most searchable encryption schemes do not consider search result diversification, resulting in information redundancy. In this paper, a verifiable diversity ranking search scheme over encrypted outsourced data is proposed while preserving privacy in… More >

Chuanrong Wu^{1, *}, Evgeniya Zapevalova¹, Yingwu Chen², Feng Li³

CMC-Computers, Materials & Continua, Vol.54, No.3, pp. 269-285, 2018, DOI:10.3970/cmc.2018.054.269

Abstract In the big data environment, enterprises must constantly assimilate big data knowledge and private knowledge by multiple knowledge transfers to maintain their competitive advantage. The optimal time of knowledge transfer is one of the most important aspects to improve knowledge transfer efficiency. Based on the analysis of the complex characteristics of knowledge transfer in the big data environment, multiple knowledge transfers can be divided into two categories. One is the simultaneous transfer of various types of knowledge, and the other one is multiple knowledge transfers at different time points. Taking into consideration the influential factors, such as the knowledge type,… More >

Chein-Shan Liu¹

CMC-Computers, Materials & Continua, Vol.21, No.1, pp. 17-40, 2011, DOI:10.3970/cmc.2011.021.017

Abstract Only the left-boundary data of temperature and heat flux are used to estimate an unknown parameter function α(x) in T_t(x,t) = ∂(α(x)T_x)/∂x + h(x,t), as well as to recover the right-boundary data. When α(x) is given the above problem is a well-known inverse heat conduction problem (IHCP). This paper solves a mixed-type inverse problem as a combination of the IHCP and the problem of parameter identification, without needing to assume a function form of α(x) a priori, and without measuring extra data as those used by other methods. We use the one-step Lie-Group Adaptive Method (LGAM) for the semi-discretizations of… More >

Liviu Marin¹

CMC-Computers, Materials & Continua, Vol.17, No.3, pp. 233-274, 2010, DOI:10.3970/cmc.2010.017.233

Abstract We investigate two algorithms involving the relaxation of either the given boundary temperatures (Dirichlet data) or the prescribed normal heat fluxes (Neumann data) on the over-specified boundary in the case of the iterative algorithm of Kozlov91 applied to Cauchy problems for two-dimensional steady-state anisotropic heat conduction (the Laplace-Beltrami equation). The two mixed, well-posed and direct problems corresponding to every iteration of the numerical procedure are solved using the method of fundamental solutions (MFS), in conjunction with the Tikhonov regularization method. For each direct problem considered, the optimal value of the regularization parameter is chosen according to the generalized cross-validation (GCV)… More >