Web-based Distributed Network Analyzer using a System Entity Structure over a Service-oriented Architecture

TL;DR: An approach to efficiently and quickly analyze a large number of network behaviors by applying System Entity Structure (SES) theory is presented and a web-based distributed simulation approach over Service-oriented Architecture (SOA) is applied.

Abstract: As a network’s uses, and especially the number of internet users, increases rapidly, an efficient system for managing large-network traffic datasets becomes an important issue. Although there are several network traffic analysis tools, such as tcpdump, Ethereal, and other applications, these tools have weaknesses, namely the limited size of files, the use of command line execution, the large memory and huge computational power requirements. In addition to these scalability limitations, both tcpdump and Ethereal have security issues. Files captured by these tools keep all of the packet information, such as internet protocol (IP) addresses, port numbers, and packet sizes. As well as basic network traffic information, the captured files contain secure information: user identification numbers (IDs) and passwords. Therefore, the captured files should not be allowed to be leaked out. However, network analyses need to be performed outside the target networks in some cases. This paper presents an approach to efficiently and quickly analyze a large number of network behaviors. This is achieved by applying System Entity Structure (SES) theory. To speed up evaluation time, a web-based distributed simulation approach over Service-oriented Architecture (SOA) is applied. Discrete Event System Specification/Service-oriented Architecture (DEVS/SOA) is used to deploy workloads into multi-servers, increasing overall system performance. A web-based distributed simulation contains two fundamental processes: distributing and analyzing among loosely coupled models through message-passing methods. The distributed simulation — allocating distributing models inside networks and assigning analyzing models outside networks — also allows the analysis of network behaviors out of networks while keeping important information secured.