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Detecting when operators achieve expert proficiency is critical for organizations that employ human–automation interaction (HAI) in operations, particularly in safety-critical settings. Training operators for complex systems demand substantial time and resources, necessitated by safety considerations and the expansive scale of these systems. Recognizing operator expertise becomes instrumental in resource optimization and training efficiency. This study explores a modeling framework for real-time analysis of HAI operator behavior and strategies. Proposing a departure from traditional assessments, the research advocates for leveraging hidden Markov models (HMMs) to provide a comprehensive portrayal of operator performance, facilitating a nuanced comparison of expert and novice strategies. Using data from a real-time strategy game, the paper details the development of HMMs and elucidates training and interface design implications. Results affirm the hypothesis that experts formulate more efficient strategies, reflected in HMMs with fewer hidden states compared to those describing novice behavior. This aligns with prior research emphasizing the organized nature of experts’ strategies. In-depth analysis delves into specific states, frequencies, and predominant strategies, revealing distinctions between experts’ offensive focus and novices’ emphasis on initial setup aspects of gameplay.

Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework to design supervisors for CPS under attacks using finite-state transducers (FSTs) to model the effects of the discrete events. FSTs can capture a general class of regular-rewriting attacks in which an attacker can nondeterministically rewrite sensing/actuation events according to a given regular relation. These include common insertion, deletion, event-wise replacement, and finite-memory replay attacks. We propose new theorems and algorithms with polynomial complexity to design resilient supervisors against these attacks. We also develop an open-source tool in Python based on the results and illustrate its applicability through a case study.

Electromagnetic jamming results in a loss of link reliability, increased energy consumption and packet delays. In the context of energy-constrained wireless networks, nodes are scheduled to maximize the common sleep duration and coordinate communication to extend their battery life. This coordination results in statistical and predictable activity patterns that may be easily detected and jammed. To eliminate spatio-temporal patterns of communication in the link and network layers, we present WisperNet, an energy-efficient anti-jamming protocol. WisperNet employs hardware-based time synchronization and lightweight cryptographic hashing for coordinated temporal randomization of slot schedules at the link layer and adapts routes to avoid static jammers in the network layer. We demonstrate that WisperNet reduces the efficiency of any statistical jammer to that of a random jammer, which has the lowest censorship-to-link utilization ratio. In the presence of a statistical jammer, WisperNet provides sub-2% packet drop ratios for link utilization up to 50%. In addition, the jammer's censorship efficiency is linear with link utilization as it is unable to extract any communication patterns. WisperNet is more efficient than low-power CSMA protocols in terms of energy, effective network throughput, reliability and delay.