Visible to the public Continuous and Adaptive Learning over Big Streaming Data for Network Security

TitleContinuous and Adaptive Learning over Big Streaming Data for Network Security
Publication TypeConference Paper
Year of Publication2019
AuthorsMulinka, Pavol, Casas, Pedro, Vanerio, Juan
Conference Name2019 IEEE 8th International Conference on Cloud Networking (CloudNet)
Date PublishedNov. 2019
PublisherIEEE
ISBN Number978-1-7281-4832-8
KeywordsAdaptation models, adaptive learning, adaptive learning models, analysis tasks, Big Data, Big Data analytics platform, big data security in the cloud, big streaming data, big-data, composability, computer network security, continual learning, Data models, Dynamic Networks and Security, learning (artificial intelligence), learning model, learning setup, machine learning, machine learning algorithms, Metrics, Microsoft Windows, network attacks, Network security, network traffic monitoring, off-the-shelf stream learning approaches, pubcrawl, resilience, Resiliency, Scalability, security, Stream Machine Learning, telecommunication traffic
Abstract

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as x 6) can be achieved by parallelizing off-the-shelf stream learning approaches.

URLhttps://ieeexplore.ieee.org/document/9064134
DOI10.1109/CloudNet47604.2019.9064134
Citation Keymulinka_continuous_2019