The Vision of Self-aware Reordering of Security Network Function Chains
L. Iffländer, J. Walter, S. Eismann, and S. Kounev. Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering, page 1--4. New York, NY, USA, ACM, (2018)
Abstract
Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.
%0 Conference Paper
%1 IfWaEiKo2018-ICPE-SSFC-Vision
%A Iffländer, Lukas
%A Walter, Jürgen
%A Eismann, Simon
%A Kounev, Samuel
%B Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering
%C New York, NY, USA
%D 2018
%I ACM
%K DECLARE DML Design_of_software_and_systems EvIDencE Multi-criteria_optimization Optimization Performance SDN Security Self-adaptive-systems Self-aware-computing descartes myown t_visionposition
%P 1--4
%T The Vision of Self-aware Reordering of Security Network Function Chains
%U https://dl.acm.org/doi/10.1145/3185768.3186309
%X Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.
@inproceedings{IfWaEiKo2018-ICPE-SSFC-Vision,
abstract = {Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.},
added-at = {2020-04-06T11:24:37.000+0200},
address = {New York, NY, USA},
author = {Iffländer, Lukas and Walter, Jürgen and Eismann, Simon and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/280bb80bf03accb238b018a2362eef71d/samuel.kounev},
booktitle = {Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering},
interhash = {acb64acabf0b836208e924ea626e7e39},
intrahash = {80bb80bf03accb238b018a2362eef71d},
keywords = {DECLARE DML Design_of_software_and_systems EvIDencE Multi-criteria_optimization Optimization Performance SDN Security Self-adaptive-systems Self-aware-computing descartes myown t_visionposition},
pages = {1--4},
publisher = {ACM},
series = {ICPE '18},
timestamp = {2022-09-15T01:06:20.000+0200},
title = {The Vision of Self-aware Reordering of Security Network Function Chains},
url = {https://dl.acm.org/doi/10.1145/3185768.3186309},
year = 2018
}