Hunting
cyberattacks: experience from the real backbone network 1ITTI
Sp. z o.o., Poznań, Poland 2Bydgoszcz
University of Science and Technology, Bydgoszcz, Poland 3Orange, Warsaw, Poland 4FernUniversität in Hagen, Germany Abstract Computer networks are
exposed to attacks which have been increasingly more effective. To counter
these emerging threats, researchers and security engineers work relentlessly
to keep up with the arms race and offer improvements to intrusion detection
systems as soon as possible. In the recent years, there has been an increase
in the proliferation of systems employing deep learning and machine learning
algorithms to detect suspicious patterns more effectively. To leverage AI
effectively in a real-world scenario of intrusion detection, a scalable
stream processing system to feed the detection algorithms with data samples in
a timely and reliable manner has to be established. In this paper, two use
cases of intrusion detection are presented. The first one shows a real-world
example of data collected by one the largest telecom operators - ORANGE. The
data was gathered for the SIMARGL project. The second use case presents the
experiments and results of intrusion detection based on the Netflow scheme. The paper also proposes a scalable
streaming architecture based on the Apache Spark and Apache Kafka
technologies. The results of the evaluation of the effectiveness of detecting
malicious behavior in network packets using several machine learning
techniques in conjunction with the stream processing framework are presented. Keywords: Machine learning, Stream processing, Intrusion
detection, Network data +: Corresponding author: Marek Pawlicki Journal of Wireless Mobile Networks, Ubiquitous
Computing, and Dependable Applications (JoWUA) Received: November 22, 2021; Accepted: May 18, 2022; Published: June
30, 2022 DOI:
10.22667/JOWUA.2022.06.30.128 |