Multi-robot systems (MRSs) have gained significant momentum as of late in the robotics community as they find application in tasks such as unknown environment exploration, distributed surveillance, and search and rescue. Operating robot teams in real world environments introduces a notion of uncertainty into the system, especially when it comes to the ability of the MRS to reliably communicate. This poses a significant challenge as a stable communication topology is the backbone of the team's ability to coordinate. Additionally, as these systems continue to evolve and integrate further into our society, a growing threat of adversarial attackers pose the risk of compromising nominal operation. As such, this dissertation aims to model the effects of uncertainty in communication on the topology of the MRS using a probabilistic interaction model. More specifically we are interested in studying a probabilistic perspective to those topologies that pertain to the security and resilience of an MRS against adversarial attacks. Having a model that is capable of capturing how probabilistic topologies may evolve over time is essential for secure and resilient planning under communication uncertainty. As a result, we develop probabilistic models, both exact and approximate, for the topological properties of system left-invertibility and (r, s)-robustness that respectively characterize the security and resilience of an MRS. In our modeling, we use binary decision diagrams, convolutional neural networks, matroid theory and more to tackle the problems related to probabilistic security and resilience where we find exact solutions, calculate bounds, solve optimization problems, and compute informative paths for exploration. / Doctor of Philosophy / When robots coordinate and interact together to achieve a collaborative task as a team, we obtain what is known as a multi-robot system or MRS for short. MRSs have several advantages over single robots. These include reliability through redundancy, where several robots can perform a given task in case one of the robots unexpectedly fails. The ability to work faster and more efficiently by working in parallel and at different locations. And taking on more complex tasks that can be too demanding for a single robot to complete. Unfortunately, the advantages of MRSs come at a cost, they are generally harder to coordinate, the action of one robot often depends on the action of other robots in the system, and they are more vulnerable to being attacked or exploited by malicious attackers who want to disrupt nominal operation. As one would expect, communication plays a very important roles in coordinating a team of robots. Unfortunately, robots operating in real world environments are subject to disturbances such as noise, obstacles, and interference that hinders the team's ability to effectively exchange information. In addition to being crucial in coordination, effective information exchange plays a major role in detecting and avoiding adversarial robots. Whenever misinformation is being spread in the team, the best way to counter such adversarial behavior is to communicate with as much well-behaving robots as possible to identity and isolate inconsistencies. In this dissertation we try to study how uncertainty in communication affects a system's ability to detect adversarial behavior, and how we can model such a phenomenon to help us account for these uncertainties when designing secure and resilient multi-robot systems.