Connected Autonomous Vehicles have great potential to improve automobile safety and traffic flow, especially in cooperative applications where perception data is shared between vehicles. However, this cooperation must be secured from malicious intent and unintentional errors that could cause accidents. Previous works typically address singular security or reliability issues for cooperative driving in specific scenarios rather than the set of errors together. In this paper, we propose CONClave - a tightly coupled authentication, consensus, and trust scoring mechanism that provides comprehensive security and reliability for cooperative perception in autonomous vehicles. CONClave benefits from the pipelined nature of the steps such that faults can be detected significantly faster and with less compute. Overall, CONClave shows huge promise in preventing security flaws, detecting even relatively minor sensing faults, and increasing the robustness and accuracy of cooperative perception in CAVs while adding minimal overhead.

Phishing is a ubiquitous and increasingly sophisticated online threat. To evade mitigations, phishers try to "cloak" malicious content from defenders to delay their appearance on blacklists, while still presenting the phishing payload to victims. This cat-and-mouse game is variable and fast-moving, with many distinct cloaking methods---we construct a dataset identifying 2,933 real-world phishing kits that implement cloaking mechanisms. These kits use information from the host, browser, and HTTP request to classify traffic as either anti-phishing entity or potential victim and change their behavior accordingly.

Ad hoc communication networks provide a robust and low-power method for sensors to return their observations to an authority. However, ensuring that the returned values accurately represent the environment being sensed poses challenges. In particular, malicious sensors controlled by an adversary may collude to return systematically misleading or falsified results. Previous work examines this challenge for weak adversaries and under strong assumptions, limiting practical applicability. In this work, we propose Pando, a novel approach for mitigating the Byzantine distributed sensor fusion problem. We introduce an approach for the decentralized creation of a forest ensemble, made up of overlapping, hierarchical message passing routes that provide robust and efficient delivery while also mitigating adversarial interference. We then leverage a modified, homomorphic Merkle tree structure to create a novel Byzantine-tolerant message passing protocol. Finally, we propose a classification-informed data fusion algorithm based on these methods. We evaluate the correctness and efficiency of our approach under several attack models, and discuss functionality improvements over the state of the art.

Smart cities have emerged as one of the most promising applications of cyber-physical systems (CPS), carrying the potential to serve the various interests of the public and private sectors at large. However, contemporary smart city infrastructure commonly uses heavily-centralized network architectures, reducing failure resilience and application flexibility. This centralization also imposes high barriers to entry for public access, limiting usage and oversight opportunities. To address these limitations, we describe Arbiter, a novel fog- and edge-based communication architecture based on the concept of a Decentralized Autonomous Organization (DAO). Arbiter aims to improve the socioeconomic equity of the local citizenry by (1) acting as a management layer for citywide CPS assets, (2) providing a compliance layer for managing human capital, and (3) offering a data protection layer to ensure that citizens retain full control of their personal data. We then analyze in detail the technical, socioeconomic, and ethical implications of Arbiter, and contextualize its role in the modern smart city.

Wireless sensor networks and other power-efficient devices fill increasingly important roles in modern society. At the same time, they also face increasing internal and external threats, such as node capture or protocol disruption by adversarial agents. Providing reliable and secure service in the face of these challenges remains an ongoing problem, and one that is only exacerbated by the computational and power constraints imposed on these devices. In this paper, we first introduce the concept of on-demand topic channels in the context of ephemeral wireless sensor networks. Then, building on this concept, we introduce three novel messaging protocols to provide secure, authenticated communication between a sensor network and an authorized user while also providing resilience from accidental or adversarial disruption. These protocols leverage homomorphic hashing in innovative ways to trade secrecy against network and computational costs in on-demand topic channel authentication. Finally, we compare and contrast the costs of these protocols, and show that hash-based protocols provide significant implementation-independent improvements to network resilience.

Data- and model-driven computer simulations are increasingly critical in many application domains. Yet, several critical data challenges remain in obtaining and leveraging simulations in decision making. Simulations may track 100s of parameters, spanning multiple layers and spatial-temporal frames, affected by complex inter-dependent dynamic processes. Moreover, due to the large numbers of unknowns, decision makers usually need to generate ensembles of stochastic realizations, requiring 10s-1000s of individual simulation instances. The situation on the ground evolves unpredictably, requiring continuously adaptive simulation ensembles. We introduce the DataStorm framework for simulation ensemble management, and demonstrate its DataStorm-FE data- and decision-flow and coordination engine for creating and maintaining coupled, multi-model simulation ensembles. DataStorm-FE enables end-to-end ensemble planning and optimization, including parameter-space sampling, output aggregation and alignment, and state and provenance data management, to improve the overall simulation process. It also aims to work efficiently, producing results while working within a limited simulation budget, and incorporates a multivariate, spatiotemporal data browser to empower decision-making based on these improved results.