Functional and Attack graph Secured HybrId Optimization of virtualized Networks Fashion's goal is to balance the functionality and security needs of the network. Functionality needs are relatively straightforward to state: a set of desired network Flows that should be carried in the network while respecting link capacity. Security is more complicated to state. We use the abstraction of attack graphs.
Fashion considers both functionality and security when deciding how to con figure the network. The functional layer treats network traffic as a multi-commodity data flow problem and provides the logic to route flows. To enable the security layer, we introduce security metrics which can be evaluated using linear programming to deliver quick calculation of risk on related networks. The security layer then integrates the risk of a con figuration to create a joint model between the two layers. This joint model (solved with integer linear program- ming) focuses on recon figuring the network.
Generates a JSON file consisting of a Fat-tree network topology, traffic demand and network vulnerabilities. This file generates clos-style Fat-tree according to the paper: "A Scalable Commodity Data Center Network Architecture" by Al-Fares,Loukissas and Vahdat. Designed to be input for Optimization framework FASHION.
(Probabilistic) Attack graphs are used to model risk. An attack graph is a labeled transition system that models an adversary's capabilities within a network and how those can be elevated by transitioning to new states via the exploitation of vulnerabilities (e.g., a weak password, a bug in a software package, the ability to guess a stack address,...). In this work, we focus on risk that is due to network confi guration.
The optimization model and network visualization code is Python2.7 compatible.
The framework can be run using python main.py <instanceFile> (if no instance filename is given,
the model will run with smallToy.json).
This will generate an image of the attack graph that shows which arcs were cut by the optimizer, as well as a window showing the network routing and firewall placement.
pip install Pillow Tk graphviz tarjan
In order to run the optimization model, Gurobi Optimization libraries are required. See gurobi.com for instructions on installing these libraries and obtaining a license key.