| Title | Towards Antifragility in Contested Environments: Using Adversarial Search to Learn, Predict, and Counter Open-Ended Threats |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Hashmi, Saad Sajid, Dam, Hoa Khanh, Smet, Peter, Chhetri, Mohan Baruwal |
| Conference Name | 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) |
| Date Published | sep |
| Keywords | adversarial search, antifragility, Autonomic Security, composability, Computational modeling, distributed computing, multi-agent systems, opponent learning, pubcrawl, reinforcement learning, resilience, Resiliency |
| Abstract | Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-* properties to be of key importance: self-exploration, self-learning and self-training. Systems should be able to efficiently self-explore - using adversarial search - the potential impact of the adversary's attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary's capabilities and attack strategies, which can be self-learned - using opponent modelling - from previous attacks and interactions. The system can self-train - using reinforcement learning - such that it evolves and improves itself as a result of being attacked. This paper discusses those visions and outlines their realisation in AWaRE, a cyber-resilient and self-adaptive multi-agent system. |
| DOI | 10.1109/ACSOS55765.2022.00032 |
| Citation Key | hashmi_towards_2022 |
Towards Antifragility in Contested Environments: Using Adversarial Search to Learn, Predict, and Counter Open-Ended Threats