Method based on fuzzy linguistic scale and FIOWGA operator for decision-making problems

In this paper, we present a fuzzy linguistic scale, which is characterized by triangular fuzzy numbers on [1/9, 9], for the comparison between two alternatives, and introduce a possibility degree formula for comparing triangular fuzzy numbers. We utilize the fuzzy linguistic scale to construct a linguistic preference matrix, and propose a fuzzy induced ordered weighted geometric averaging (FIOWGA) operator to aggregate linguistic preference information. A method based on the fuzzy linguistic scale and FIOWGA operator for decision-making problems is presented. Finally, an illustrative example is given to verify the developed method and to demonstrate its feasibility and effectiveness.

Research on internet traffic classification techniques using supervised machine learning

Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications （e. g. Peer-to-Peer） using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning （ML） based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments： （1） The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. （2） The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. （3） Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results.

Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.

Design and Implementation of Security Solution for Computer Network

This article analyzes the problem of computer network security, and design scheme of the network security system. The scheme uses advanced network security technologies, includes a complete set of physical isolation, desktop system security, virus protection, identity authentication, access control, information encryption, message integrity check, non-repudiation, security audit, intrusion detection, vulnerability scanning, electromagnetic leakage emission protection, security management and other security technology and management measures, the purpose is to establish a complete, multi-level three-dimensional, network security defense system.

议事员会

七月八日为议事员常会期.当于是日中午.在一枝香与本社附设中华职业学校经济校董举行联席会议.除报告社.校常务而外.提出社.校上年度经费决算案.当公扒张云搏.王一亭两君会同原推经济审查员加以审核.又提出第七届年会交议设立本社基金林场案.由原提议人傅志章等列席说明.

HASM-AD Algorithm Based on the Sequential Least Squares

The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.

Benchmark solutions for MHD solver development

A benchmark solution is of great importance in validating algorithms and codes for magnetohydrodynamic(MHD) flows.Hunt and Shercliff's solutions are usually employed as benchmarks for MHD flows in a duct with insulated walls or with thin conductive walls,in which wall effects on MHD are represented by the wall conductance ratio.With wall thickness resolved,it is stressed that the solution of Sloan and Smith's and the solution of Butler's can be used to check the error of the thin wall approximation condition used for Hunt's solutions.It is noted that Tao and Ni's solutions can be used as a benchmark for MHD flows in a duct with wall symmetrical or unsymmetrical,thick or thin.When the walls are symmetrical,Tao and Ni's solutions are reduced to Sloan and Smith's solution and Butler's solution,respectively.