# Minimal Schedule with Minimal Number of Agents in Attack-Defence Trees This repository hosts the results for the paper. ## Clone this repository: ``` git clone https://depot.lipn.univ-paris13.fr/parties/publications/minimal-scheduling.git && cd minimal-scheduling ``` ## Folder Structure ``` . └── results # folder with the ADTree models and the minimal assignments ``` ## Results The minimal scheduling results can be found in the `results` folder. The reader can found the sources and binaries of the `adt2amas` tool [here](https://depot.lipn.univ-paris13.fr/parties/tools/adt2amas/-/releases/v2.1.2). In order to reproduce the results, the command to be executed is the following : ``` ./adt2amas minimal --model results//model/.txt ``` Below we show some case studies. ### Forestalling a software release (forestall) This model is based on a real-world instance [1]. It models an attack to the intellectual property of a company C, by an unlawful competitor company U aiming at being “first to the market.” Following [2], software extraction from C takes place before U builds it into its own product, and U must deploy to market before C, which takes place after U has integrated the stolen software into its product. #### ADTree model ![forestall ADTree](results/forestall/model/forestall.png) #### Minimal Scheduling The reader can find the 4 possible assignments [here](results/forestall/assignment). ### Obtain admin privileges (gain-admin) To gain administrative privileges in a UNIX system, an attacker needs either physical access to an already logged-in console or remote access via privilege escalation (attacking SysAdmin). This case study [3] exhibits a mostly branching structure: all gates but one are disjunctions in the original tree of [3]. We enrich this scenario with the SAND gates of [2], and further add reactive defences #### ADTree model ![gain-admin ADTree](results/gain-admin/model/gain-admin.png) #### Minimal Scheduling The reader can find the 16 possible assignments [here](results/gain-admin/assignment). ### Interrupted #### ADTree model ![interrupted ADTree](results/interrupted/model/interrupted.png) #### Minimal Scheduling ![interrupted Assignment](results/interrupted/assignment/interrupted_scheduling_1.png) ### Compromise IoT device (iot-dev) This model describes an attack to an Internet-of-Things (IoT) device either via wireless or wired LAN. Once the attacker gains access to the private network and has acquired the corresponding credentials, it can exploit a software vulnerability in the IoT device to run a malicious script. Our ADTree adds defence nodes on top of the attack trees used in [4]. #### ADTree model ![iot-dev ADTree](results/iot-dev/model/iot-dev.png) #### Minimal Scheduling ![iot-dev Assignment](results/iot-dev/assignment/iot_dev_scheduling_1.png) ### Last #### ADTree model ![last ADTree](results/last/model/last.png) #### Minimal Scheduling ![last Assignment](results/last/assignment/last_scheduling_1.png) ### Toy Example #### ADTree model ![toy-example ADTree](results/toy-example/model/toy-example.png) #### Minimal Scheduling ![toy-example Assignment](results/toy-example/assignment/toy_example_scheduling_1.png) ### Treasure Hunters It models thieves that try to steal a treasure in a museum. To achieve their goal, they first must access the treasure room, which involves bribing a guard (b), and forcing the secure door (f). Both actions are costly and take some time. Two coalitions are possible: either a single thief has to carry out both actions, or a second thief could be hired to parallelise b and f. After these actions succeed the attacker/s can steal the treasure (ST), which takes a little time for opening its display stand and putting it in a bag. If the two-thieves coalition is used, we encode in ST an extra 90 € to hire the second thief — the computation function of the gate can handle this plurality — else ST incurs no extra cost. Then the thieves are ready to flee (TF), choosing an escape route to get away (GA): this can be a spectacular escape in a helicopter (h), or a mundane one via the emergency exit (e). The helicopter is expensive but fast while the emergency exit is slower but at no cost. Furthermore, the time to perform a successful escape could depend on the number of agents involved in the robbery. Again, this can be encoded via computation functions in gate GA. As soon as the treasure room is penetrated (i.e. after b and f but before ST) an alarm goes off at the police station, so while the thieves flee the police hurries to intervene (p). The treasure is then successfully stolen iff the thieves have fled and the police failed to arrive or does so too late. This last possibility is captured by the condition associated with the treasure stolen gate (TS), which states that the arrival time of the police must be greater than the time for the thieves to steal the treasure and go away. #### ADTree model ![Treasure Hunters ADTree](results/treasure-hunters/model/treasure-hunters.png) #### Minimal Scheduling ![Treasure Hunters Assignment](results/treasure-hunters/assignment/treasure_hunters_scheduling_1.png) ### Tricky #### ADTree model ![tricky ADTree](results/tricky/model/tricky.png) #### Minimal Scheduling ![tricky Assignment](results/tricky/assignment/tricky_scheduling_1.png) ## Authors - Jaime Arias (LIPN, CNRS UMR 7030, Université Sorbonne Paris Nord) - Wojciech Penczek (Institute of Computer Science, PAS, Warsaw University of Technology) - Laure Petrucci (LIPN, CNRS UMR 7030, Université Sorbonne Paris Nord) - Teofil Sidoruk (Institute of Computer Science, PAS, Warsaw University of Technology) ## Abstract ## References [1] A. Buldas, P. Laud, J. Priisalu, M. Saarepera, and J. Willemson. Rational Choice of Security Measures Via Multi-parameter Attack Trees. In Critical Information Infrastructures Security, pages 235–248. Springer, 2006. [2] R. Kumar, E. Ruijters, and M. Stoelinga. Quantitative attack tree analysis via priced timed automata. In FORMATS 2015, volume 9268 of LNCS, pages 156– 171. Springer, 2015. [3] J. D. Weiss. A system security engineering process. In Proceedings of the 14th National Computer Security Conference, pages 572–581, 1991. [4] R. Kumar, S. Schivo, E. Ruijters, B. M. Yildiz, D. Huistra, J. Brandt, A. Rensink, and M. Stoelinga. Effective analysis of attack trees: A model-driven approach. In Fundamental Approaches to Software Engineering, pages 56–73. Springer, 2018.