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Article

Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation

1
Department of Information & Communications Systems Engineering, University of the Aegean, Neo Karlovasi, 83200 Samos, Greece
2
Electrical and Computer Engineering Department, Hellenic Mediterranean University, Herakleion, 71410 Crete, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Alexios Mylonas and Nikolaos Pitropakis
Sensors 2021, 21(14), 4939; https://doi.org/10.3390/s21144939
Received: 29 June 2021 / Revised: 14 July 2021 / Accepted: 15 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Cyber Situational Awareness in Computer Networks)
The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems’ vulnerabilities and alleviating the impact of these threats, this paper presents a machine learning based situational awareness framework that detects existing and newly introduced network-enabled entities, utilizing the real-time awareness feature provided by the SDN paradigm, assesses them against known vulnerabilities, and assigns them to a connectivity-appropriate network slice. The assessed entities are continuously monitored by an ML-based IDS, which is trained with an enhanced dataset. Our endeavor aims to demonstrate that a neural network, trained with heterogeneous data stemming from the operational environment (common vulnerability enumeration IDs that correlate attacks with existing vulnerabilities), can achieve more accurate prediction rates than a conventional one, thus addressing some aspects of the situational awareness paradigm. The proposed framework was evaluated within a real-life environment and the results revealed an increase of more than 4% in the overall prediction accuracy. View Full-Text
Keywords: situational awareness; intrusion detection systems; vulnerability assessment; machine learning; SDN; software defined networking situational awareness; intrusion detection systems; vulnerability assessment; machine learning; SDN; software defined networking
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MDPI and ACS Style

Nikoloudakis, Y.; Kefaloukos, I.; Klados, S.; Panagiotakis, S.; Pallis, E.; Skianis, C.; Markakis, E.K. Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation. Sensors 2021, 21, 4939. https://doi.org/10.3390/s21144939

AMA Style

Nikoloudakis Y, Kefaloukos I, Klados S, Panagiotakis S, Pallis E, Skianis C, Markakis EK. Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation. Sensors. 2021; 21(14):4939. https://doi.org/10.3390/s21144939

Chicago/Turabian Style

Nikoloudakis, Yannis, Ioannis Kefaloukos, Stylianos Klados, Spyros Panagiotakis, Evangelos Pallis, Charalabos Skianis, and Evangelos K. Markakis 2021. "Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation" Sensors 21, no. 14: 4939. https://doi.org/10.3390/s21144939

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