Feature Engineering using Bio-Inspired Methods for the Internet Data Analytics

使用仿生方法进行互联网数据分析的特征工程

• 批准号: 341811-2012
• 负责人: Raahemi, Bijan
• 金额: $ 1.53万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595487
• 关键词: Feature; Engineering; using; Bio; Inspired

Accurate classification of the Internet traffic has been a permanent concern for the Internet Service Providers for the two main reasons: network security; and network resource allocation. With the growth of the Internet traffic, in terms of the number and type of applications, traditional identification methods for the Internet data analytics face serious challenges when the newer and/or malicious applications use randomly selected ports to communicate making them increasingly difficult to identify. Thus, analysis of the Internet traffic based on its statistical behaviors using machine learning, evolutionary methods, and data mining techniques have gained much attention in academia and industry. For this, the Internet data must be captured and analyzed. The features extracted from the communications networks are very diverse and the datasets are typically large and multidimensional. Consequently, efficient transformation and computation of the significant features for adequate data analytics are important research tasks to focus on.

互联网流量的准确分类一直是互联网服务提供商的一个永久关注的问题，主要有两个原因：网络安全和网络资源分配。随着互联网流量的增长，在应用程序的数量和类型方面，当新的和/或恶意的应用程序使用随机选择的端口进行通信时，用于互联网数据分析的传统识别方法面临严重的挑战，使得它们越来越难以识别。因此，使用机器学习、进化方法和数据挖掘技术，基于其统计行为的互联网流量分析在学术界和工业界受到了广泛的关注。为此，必须捕获和分析互联网数据。从通信网络中提取的特征是非常多样的，并且数据集通常是大的和多维的。因此，有效的转换和计算的重要功能，充分的数据分析是重要的研究任务，重点放在。