A Monitoring and Threat Detection System Using Stream Processing as a Virtual Function for Big Data

  • Martin E. Andreoni Lopez Samsung Research
  • Otto Carlos Muniz Bandeira Duarte UFRJ
  • Guy Pujolle Université Pierre et Marie Curie - Paris 6


The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. As a consequence, fast real-time threat detection is mandatory for security guarantees. In addition, Network Function Virtualization (NFV) provides new opportunities for efficient and low-cost security solutions. We propose a fast and efficient threat detection system based on stream processing and machine learning algorithms. The main contributions of this work are i) a novel monitoring threat detection system based on stream processing; ii) two datasets, first a dataset of synthetic security data containing both legitimate and malicious traffic, and the second, a week of real traffic of a telecommunications operator in Rio de Janeiro, Brazil; iii) a data pre-processing algorithm, a normalizing algorithm and an algorithm for fast feature selection based on the correlation between variables; iv) a virtualized network function in an open-source platform for providing a real-time threat detection service; v) near-optimal placement of sensors through a proposed heuristic for strategically positioning sensors in the network infrastructure, with a minimum number of sensors; and, finally, vi) a greedy algorithm that allocates on demand a sequence of virtual network functions.


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ANDREONI LOPEZ, Martin E.; MUNIZ BANDEIRA DUARTE, Otto Carlos ; PUJOLLE, Guy . A Monitoring and Threat Detection System Using Stream Processing as a Virtual Function for Big Data. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 2. , 2019, Gramado. Anais Estendidos do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Porto Alegre: Sociedade Brasileira de Computação, sep. 2019 . p. 209-216. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc_estendido.2019.7789.