Description: 实现网络流量分析，网络抓包，可以用饼图，曲线图等多种图形绘制出流量，同时可以分析访问者的ＩＰ地址及端口号-network traffic analysis, network capturing Packet can be pie, charts and other graphics drawn flow, visitors also can analyze the IP address and port number Platform: |
Size: 227328 |
Author:王洪斌 |
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Description: 网络流量采集及分析软件，可以设定规则来获取你所需要的流量特征。-network traffic acquisition and analysis software, can set rules to obtain what you need the flow characteristics. Platform: |
Size: 990208 |
Author:杨明 |
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Description: 实现网络流量的分析，是一份很好的代码，是用delphi编写的-Realization of network traffic analysis, is a very good code, is prepared by delphi Platform: |
Size: 269312 |
Author:李杰 |
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Description: TCP 吞吐量是 TCP 性能的一个重要的端到端性能指标，本程序的功能是对实验数据进行分析，获得TCP流量传输过程中的各项参数！-TCP throughput performance of TCP is an important indicator of end-to-end performance, the function of this process is the analysis of experimental data, access to TCP traffic in the process of transmission of the parameters! Platform: |
Size: 6144 |
Author:pengjikui |
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Description: 总结近年视频传输的应用成果，分析视频传输的关键技术，给出QoS定义与视频传输的关联-Video Traffic Analysis and QoS management: Providing and analysis the Video Traffic,defining the QoS, point out the relation between QoS and Video Traffic. Platform: |
Size: 9054208 |
Author:hhs |
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Description: 针对某个网络流量数据进行分析详细示例，包括实验报告和matlab详细源码程序-For a detailed analysis of network traffic data sample, including lab reports and detailed source program matlab Platform: |
Size: 6957056 |
Author:李枫 |
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Description: IP包流量分析程序,有助于通过分析网络流量IP packet traffic analysis program, can help by analyzing network traffic-IP packet traffic analysis program, can help by analyzing network traffic Platform: |
Size: 102400 |
Author:iewwhat |
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Description: 利用NetFlow Simulator产生仿真网络流量，用Java NetFlow Collect-Analyzer采集数据并导入数据库。在此基础上针对相关数据作出分析和展示，并提供相关预测（如异常检测）。-we develop the Network Traffic Analysis System based on Cisco NetFlow. The project provide user-friendly web page, it contains charts ,tables and PDF files. Platform: |
Size: 4105216 |
Author:fhh |
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Description: With the rapid growth of the Internet of Things (IoT), the
deployment, management, and identification of IoT devices that are connected
to networks become a big concern. Consequently, they emerge as a
prominent challenge either for mobile network operators who try to offer
cost-effective services tailored to IoT market, or for network administrators
who aim to identify as well reduce costs processing and optimize
traffic management of connected environments. In order to achieve high
accuracy in terms of reliability, loss and response time, new devices real
time discovery techniques based on traffic characteristics are mandatory
in favor of the identification of IoT connected devices.
Therefore, we design GBC−IoT, a group-based machine learning approach
that enables to identify connected IoT devices through network
traffic analysis. By leveraging well-known machine learning algorithms,
GBC−IoT framework identifies and categorizes IoT devices into three
classes with an overall accuracy equals to roughly 99.98%. Therefore,
GBC−IoT can efficiently identify IoT devices with less processing overhead
compared to previous studies. Platform: |
Size: 827167 |
Author:elmustafasayed@gmail.com |
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Search - traffic analysis