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Determining an individual’s strategic reasoning capability based solely on choice data is a complex task. This complexity arises because sophisticated players might have non-equilibrium beliefs about others, leading to non-equilibrium actions. In our study, we pair human participants with computer players known to be fully rational. This use of robot players allows us to disentangle limited reasoning capacity from belief formation and social biases. Our results show that, when paired with robots, subjects consistently demonstrate higher levels of rationality, compared to when paired with human players. Furthermore, players’ rationality levels are relatively stable across games when paired with robot players, even though those with intermediate rationality levels exhibit inconsistency across games. Leveraging our experimental design, we identify and document potential causes of this inconsistency. 

Internet of Things (IoT) devices have increased drastically in complexity and prevalence within the last decade. Alongside the proliferation of IoT devices and applications, attacks targeting them have gained popularity. Recent large-scale attacks such as Mirai and VPNFilter highlight the lack of comprehensive defenses for IoT devices. Existing security solutions are inadequate against skilled adversaries with sophisticated and stealthy attacks against IoT devices. Powerful provenance-based intrusion detection systems have been successfully deployed in resource-rich servers and desktops to identify advanced stealthy attacks. However, IoT devices lack the memory, storage, and computing resources to directly apply these provenance analysis techniques on the device. This paper presents ProvIoT, a novel federated edge-cloud security framework that enables on-device syscall-level behavioral anomaly detection in IoT devices. ProvIoT applies federated learning techniques to overcome data and privacy limitations while minimizing network overhead. Infrequent on-device training of the local model requires less than 10% CPU overhead; syncing with the global models requires sending and receiving 2MB over the network. During normal offline operation, ProvIoT periodically incurs less than 10% CPU overhead and less than 65MB memory usage for data summarization and anomaly detection. Our evaluation shows that ProvIoT detects fileless malware and stealthy APT attacks with an average F1 score of 0.97 in heterogeneous real-world IoT applications. ProvIoT is a step towards extending provenance analysis to resource-constrained IoT devices, beginning with well-resourced IoT devices such as the RaspberryPi, Jetson Nano, and Google TPU. 

We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and whitebox attack scenarios. 

We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios. 

We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios.