Hidden communication systems (HCS) embed covert messages within ordinary network activity to hide the presence of communication. In practice, the undetectability of an HCS is typically evaluated using ad hoc traffic statistics or specific detectors, making security claims tightly coupled to experimental setups and implicit adversarial assumptions. In this work, we formalize undetectability as the statistical indistinguishability of observable execution traces under two deployments: a baseline system without hidden communication and an HCS deployment carrying covert traffic. Undetectability is expressed as a bound on a quantitative measure of distance between the trace distributions induced by these two executions. We develop Maude-HCS, an executable modeling and analysis framework that provides a principled and executable foundation for reasoning about undetectability-performance tradeoffs in complex HCS designs. Maude-HCS allows designers to specify protocol behavior, adversary observables, and environmental assumptions, and to generate Monte Carlo samples from the induced trace distributions. We demonstrate that Maude-HCS can be used to audit claims of undetectability by estimating the true and false positive rates of a statistical test and converting these estimates into lower bounds on undetectability measures such as KL divergence. This enables systematic evaluation of detectability and its tradeoffs with performance under explicitly stated modeling assumptions. Finally, we evaluate Maude-HCS on proof-of-concept tunneling-based HCS instantiations and validate model predictions against measurements from a physical testbed. For passive adversaries observing timing and traffic statistics, we quantify how undetectability and performance vary with protocol configuration, background traffic, and network loss, and demonstrate strong semantic alignment between model-based guarantees and empirical results.

Abstract Given the complexity of cyber-physical systems (CPS), such as swarms of drones, often deviations, from a planned mission or protocol, occur which may in some cases lead to harm and losses. To increase the robustness of such systems, it is necessary to detect when deviations happen and diagnose the cause(s) for a deviation. We build on our previous work on soft agents, a formal framework based on using rewriting logic for specifying and reasoning about distributed CPS, to develop methods for diagnosis of CPS at design time. We accomplish this by (1) extending the soft agents framework with Fault Models; (2) proposing a protocol specification language and the definition of protocol deviations; and (3) development of workflows/algorithms for detection and diagnosis of protocol deviations. Our approach is partially inspired by existing work using counterfactual reasoning for fault ascription. We demonstrate our machinery with a collection of experiments.

We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocols in both single-swarm and multi-swarm cases.

Reconciliation of replicated data objects is tedious and error-prone in eventually consistent systems. Syntactic reconciliation techniques use serializability or causality as the basis for conflict-detection and resolution. These techniques are fast and efficient and the resolutions can be directly managed at the database side. However, for use cases where conflict resolution demands knowledge of the application semantics, domain-specific semantic reconciliation is required. Semantic reconciliation techniques often involve maintaining all possible values and perform the resolution via domain-specific knowledge at the client side. This involves additional cost in terms of network bandwidth and latency, and considerable complexity. In this paper, we discuss the motivation and design of a novel data type called priority register that implements a domain-specific conflict detection and resolution scheme directly at the database side, while leaving open the option of additional reconciliation at the application level. Our approach uses the notion of an application-defined replacement ordering and we show that a data type parameterized by such an order can provide an efficient solution for applications that demand domain-specific conflict resolution. We also describe the proof-of-concept implementation of the priority register inside the Cassandra distributed storage system.

In this article, we propose a cross-layer algorithm/architecture coexploration for wireless multimedia systems to coordinate interactions among sublayer optimizers for improvements in energy/QoS/reliability. By exploiting the inherent redundancy in wireless multimedia systems, we generate an expanded design space over traditional layer-specific approaches. Specifically, we control the error resilient encoder at the application layer to provide awareness of architectural exploration at the physical layer allowing new design points with lower power consumption via aggressive voltage scaling. While trying to reduce energy consumption, the fault tolerant technique compensates the effect of the hardware and network errors due to aggressive voltage scaling and lossy transmission, respectively. Our experiments on H.263 video over a WCDMA communication system demonstrate that coexploration enlarges the feasible design space, which results in significant power savings of more than 20% in the WCDMA modem.

Resource-limited mobile embedded systems can benefit greatly from dynamic adaptation of system parameters. We propose a novel approach that employs iterative tuning using lightweight formal verification at runtime with feedback for dynamic adaptation. One objective of this approach is to enable trade-off analysis across multiple layers (e.g., application, middleware, OS) and predict the possible property violations as the system evolves dynamically over time. Specifically, an executable formal specification is developed for each layer of the mobile system under consideration. The formal specification is then analyzed using statistical property checking and statistical quantitative analysis, to determine the impact of various resource management policies for achieving desired timing/QoS properties. Integration of formal analysis with dynamic behavior from system execution results in a feedback loop that enables model refinement and further optimization of policies and parameters. We demonstrate the applicability of this approach to the adaptive provisioning of resource-limited distributed real-time systems using a mobile multimedia case study.

Networked Cyber-Physical Systems (NCPSs) present many challenges since they require a tight combination with the physical world as well as a balance between autonomous operation and coordination among heterogeneous nodes. These fundamental challenges range from how NCPSs are architected, implemented, composed, and programmed to how they can be validated. In this paper, we describe a new paradigm for programming an NCPS that enables users to specify their needs and nodes to contribute capabilities and resources. This new paradigm is based on the partially ordered knowledge-sharing model that makes explicit the abstract structure of a computation in space and time. Based on this model, we propose an application framework that provides a uniform abstraction for a wide range of NCPS applications, especially those concerned with distributed sensing, optimization, and control. The proposed framework provides a generic service to represent, manipulate, and share knowledge across the network under minimal assumptions on connectivity. Our framework is tested on a new distributed version of an evolutionary optimization algorithm that runs on a computing cluster and is also used to solve a dynamic distributed optimization problem in a simulated NCPS that uses mobile robots as controllable data mules.

Data is routinely created, disseminated, and processed in distributed systems that span multiple administrative domains. To maintain accountability while the data is transformed by multiple parties, a consumer must be able to check the lineage of the data and deem it trustworthy. If integrity is not ensured, the consequences can be significant, particularly when the data cannot easily be reproduced. Verifying the provenance of a piece of data generated using inputs from multiple administrative domains is likely to require the use of numerous public keys that originate at external institutions. Current methods for verifying the integrity of such data from other users will not scale for provenance metadata since scores of verifications may be needed to validate a single file's lineage graph. We describe Mendel, a protocol with a three-pronged strategy that combines eager signature verification, lazy trust establishment, and cryptographic ordering witnesses to yield fast lineage verification in distributed multi-domain environments. Further, we show how decisional lineage queries, that is whether one file is the ancestor of the other, can be answered with high probability in constant time.

Current projects that automate the collection of provenance information use a centralized architecture for managing the resulting metadata - that is, provenance is gathered at remote hosts and submitted to a central provenance management service. In contrast, we are developing a completely decentralized system with each computer maintaining the authoritative repository of the provenance gathered on it. Our model has several advantages, such as scaling to large amounts of metadata generation, providing low-latency access to provenance metadata about local data, avoiding the need for synchronization with a central service after operating while disconnected from the network, and letting users retain control over their data provenance records. We describe the SPADE project's support for tracking data provenance in distributed environments, including how queries can be optimized with provenance sketches, pre-caching, and caching.

Adaptive resource management is critical to ensuring the quality of real-time distributed applications, particularly for energy-constrained mobile handheld devices. In this context, an optimization that simultaneously considers multiple layers (e.g., application, middleware, operating system) needs to be developed for continuous adaptation of system parameters. The tuning of system parameters greatly affects the system's ability to meet QoS requirements, and also directly affects the energy consumption and system robustness. We present a novel approach to developing cross-layer optimization for resource limited real-time distributed systems, based on a constraint refinement technique combined with formal specification and feedback from system implementation. Our approach tunes the parameters in a compositional manner allowing coordinated interaction among sub-layer optimizers that enables holistic cross-layer optimization. We present experiments on a realistic multimedia application which demonstrate that constraint refinement enables us to generate robust and near optimal parameter settings. The constraint language can be used as an interface for composition by encapsulating the details of local optimization algorithms.

Increasing exponentially with each technology generation, hardware-induced soft errors pose a significant threat for the reliability of mobile multimedia devices. Since traditional hardware error protection techniques incur significant power and performance overheads, this paper proposes a cooperative cross-layer approach that exploits existing error control schemes at the application layer to mitigate the impact of hardware defects. Specifically, we propose error detection codes in hardware, drop and forward recovery in middleware, and error-resilient video encoding at the application level to effectively and efficiently combat soft errors with minimal overheads. Experimental evaluation on standard test video streams demonstrates that our cooperative error-aware method for video encoding improves performance by 60% and energy consumption by 58% with even better reliability at the cost of only 3% quality degradation on average, as compared to an error correction code based hardware protection technique. Combining intelligent schemes to select a recovery mechanism can guide system designers to trade off multiple constraints such as performance, power, reliability, and QoS.

Real-time multimedia applications are increasingly being mapped onto MPSoC (multiprocessor system-on-chip) platforms containing hardware--software IPs (intellectual property), along with a library of common scheduling policies such as EDF, RM. The choice of a scheduling policy for each IP is a key decision that greatly affects the design's ability to meet real-time constraints, and also directly affects the energy consumed by the design. We present a cosynthesis framework for design space exploration that considers heterogeneous scheduling while mapping multimedia applications onto such MPSoCs. In our approach, we select a suitable scheduling policy for each IP such that system energy is minimized—our framework also includes energy-reduction techniques utilizing dynamic power management. Experimental results on a realistic multimode multimedia terminal application demonstrate that our approach enables us to select design points with up to 60.5% reduced energy for a given area constraint, while meeting all real-time requirements. More importantly, our approach generates a tradeoff space between energy and cost allowing designers to comparatively evaluate multiple system level mappings.