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

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。 该研究计划的目标是探索创新的算法，用于特征工程和互联网数据的分析，使用生物启发和机器学习方法，包括人工免疫系统和遗传算法。我们将设计和分析一个架构，包括一个功能工程模块，一个分类器模块，和一个再训练机制。我们将探索围绕生物启发算法构建的新方法，包括基于类依赖和类独立的遗传映射，以及基于人工免疫系统的异常检测，同时考虑不平衡数据和训练数据集中缺乏足够的标记。我们将探讨在计算机网络中的两个重要的应用程序中所提出的算法的能力：（一）入侵检测系统和异常检测网络安全;和（B）协议识别的互联网流量的资源分配和服务质量保证。

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. The objective of this research program is to explore innovative algorithms for feature engineering and analysis of the Internet data using bio-inspired and machine learning approaches including Artificial Immune Systems and Genetic Algorithms. We will devise and analyze an architecture comprising of a feature engineering module, a classifier module, and a re-training mechanism. We will explore new methods build around bio-inspired algorithms including class-dependent and class-independent genetic-based mapping, as well as artificial immune systems-based anomaly detection while considering imbalanced data and lack of sufficient labeling in the training dataset. We will explore the competency of the proposed algorithms in two important applications in computer networking: (a) intrusion detection systems and anomaly detection for network security; and (b) protocol identification of the Internet traffic for resource allocation and quality of service assurance.