Autonomous Model Update Scheme for Deep Learning-Based Network Traffic Classifiers

Network traffic classification is essential in network management and measurement in access networks, e.g., network intrusion detection, network resource allocation, etc. State-of-the-art Deep Learning based classifiers achieve high classification accuracy even when dealing with encrypted data packets. Such classifiers would need to be updated when a new application appears in the network traffic. However, it is challenging to build and label a dataset of the unknown application so that the current network traffic classifier can be updated. In this paper, we propose an autonomous model update scheme to (i) build a dataset of new application packets from active network traffic; and (ii) update the current network traffic classifier. In particular, the core of the proposed scheme is a discriminator includes a statistical filter and a Convolutional Neural Network (CNN) based binary classifier to filter and build a dataset of new application packets from active network traffic. Evaluation is conducted based on an open dataset (ISCX VPN-nonVPN dataset). The results demonstrated that our proposed autonomous classifier update scheme can successfully filter packets of a new application from network traffic and build a new training dataset. Moreover, the packet classifier can be effectively updated through transfer learning. The success of the proposed update scheme can be adopted in the access network for efficient and adaptive network measurement and management.https://ecommons.udayton.edu/stander_posters/2844/thumbnail.jp

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future