TY - GEN
T1 - Detecting workload-based and instantiation-based economic denial of sustainability on 5G environments
AU - Vidal, Jorge Maestre
AU - Sotelo Monge, Marco Antonio
AU - García Villalba, Luis Javier
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - This paper reviews the Economic Denial of Sustainability (EDoS) problem in emerging network scenarios. The performed research studied them in context of adaptive approaches grounded on self-organizing networks (SON) and Network Function Virtualization (NFV). In particular, two novel threats were reviewed in depth: Workload-based EDoS (W-EDoS) and Instantiation-based EDoS (I-EDoS). With the aim to contribute to their mitigation a security architecture with network-based intrusion detection capabilities is proposed. This architecture implements machine learning techniques, network behaviour prediction, adaptive thresholding methods, and productivity-based clustering for detecting entropy-based anomalies based on the observed workload (W-EDoS) or suspicious variations of the productivity observed at the virtual instances (I-EDoS). A detailed experimentation has been conducted considering different calibration parameters under different network scenarios, on which the security architecture has been assessed. The results have proven good accuracy levels, hence demonstrating the proposal effectiveness.
AB - This paper reviews the Economic Denial of Sustainability (EDoS) problem in emerging network scenarios. The performed research studied them in context of adaptive approaches grounded on self-organizing networks (SON) and Network Function Virtualization (NFV). In particular, two novel threats were reviewed in depth: Workload-based EDoS (W-EDoS) and Instantiation-based EDoS (I-EDoS). With the aim to contribute to their mitigation a security architecture with network-based intrusion detection capabilities is proposed. This architecture implements machine learning techniques, network behaviour prediction, adaptive thresholding methods, and productivity-based clustering for detecting entropy-based anomalies based on the observed workload (W-EDoS) or suspicious variations of the productivity observed at the virtual instances (I-EDoS). A detailed experimentation has been conducted considering different calibration parameters under different network scenarios, on which the security architecture has been assessed. The results have proven good accuracy levels, hence demonstrating the proposal effectiveness.
KW - Economical denial of sustainability
KW - Information security
KW - Intrusion detection systems
KW - Network function virtualization
KW - Self-organizing networks
UR - https://www.scopus.com/pages/publications/85055283272
U2 - 10.1145/3230833.3233247
DO - 10.1145/3230833.3233247
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85055283272
T3 - ACM International Conference Proceeding Series
BT - ARES 2018 - 13th International Conference on Availability, Reliability and Security
PB - Association for Computing Machinery
T2 - 13th International Conference on Availability, Reliability and Security, ARES 2018
Y2 - 27 August 2018 through 30 August 2018
ER -