Article ; Online: Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation.
2021 Volume 21, Issue 14
Abstract: 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 ... ...
Abstract | 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. |
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MeSH term(s) | Awareness ; Computer Security ; Machine Learning ; Neural Networks, Computer |
Language | English |
Publishing date | 2021-07-20 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2052857-7 |
ISSN | 1424-8220 ; 1424-8220 |
ISSN (online) | 1424-8220 |
ISSN | 1424-8220 |
DOI | 10.3390/s21144939 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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