We study online privacy-preserving anomaly detection in a setting in which the data are distributed over a network and locally sensitive to each node, and a probabilistic data model is unknown. We design and analyze a data-driven solution scheme where each node observes a high-dimensional data stream for which it computes a local outlierness score. This score is then perturbed, encrypted, and sent to a network operator. The network operator then decrypts an aggregate statistic over the network and performs online network anomaly detection via the proposed generalized cumulative sum (CUSUM) algorithm. We derive an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Additionally, we derive an asymptotic upper bound and asymptotic approximation for the average detection delay of the proposed algorithm under a certain anomaly. We show the analytical tradeoff between the anomaly detection performance and the differential privacy level, controlled via the local perturbation noise. Experiments illustrate that the proposed algorithm offers a good tradeoff between privacy and quick anomaly detection against the UDP flooding and spam attacks in a real Internet of Things (IoT) network.

Simulink and Stateflow are tools for Model-Based Design that support a variety of mechanisms for modeling hybrid dynamics. Each of these tools has different strengths. In this paper, a new modeling construct is presented that combines these strengths to enable graphical modeling of hybrid dynamics within a single Stateflow chart. A new type of Stateflow state that acts as a Simulink subsystem is developed to facilitate graphical modeling of continuous dynamics using Simulink blocks inside Stateflow. Remote textual and graphical state access using new state-accessor blocks enables continuous states to be used in transition guards and reset actions. Key features of this new formalism are illustrated using various examples with hybrid dynamics.

No abstract available.

The physics of technical systems, such as embedded and cyber-physical systems, is frequently modeled using the notion of continuous time. The underlying continuous phenomena may, however, occur at a time scale much faster than the system behavior of interest. In such situations, it is desirable to approximate the detailed continuous-time behavior by discontinuous change. Two classes of discontinuous change can be identified: pinnacles and mythical modes. This work shows how pinnacles are well modeled using a hyperreal notion of time while a superdense notion of time applies well to mythical modes. Thus, the combination, called hyperdense time, is proposed to allow for the expression of the semantics of both pinnacles and mythical modes. Further, the hyperdense semantic domain is translated into a computational representation as a three-dimensional model of time. In particular, continuous-time behavior is mapped onto floating point numbers, while the mythical mode and pinnacle event iterations each map onto an integer dimension. A modified Newton's cradle is used as a case study and to illustrate the computational implementation.

In the design of engineered systems two types of models are used: (i) analysis models and (ii) system models. The system models are primary deliverables between design stages whereas analysis models are employed within a design stage. Sensitivity analysis studies the behavior of the system under small parameter variations which proves to be useful in design. To enable sensitivity analysis in verification of hybrid dynamic systems that model industry-size problems, support for simulation-based methods is desired. The computational semantics for simulation of corresponding analysis models must then be consistent with the computational semantics of the system models. A method is presented that enables direct sensitivity analysis on system models via an implementation in the Simulink(R) software. The approach relies on the existing ordinary differential equation solver of Simulink and the block-by-block analytic Jacobian computation to provide the analytic Jacobian for solving the sensitivity equations. Results of a prototype implementation show that sensitivity analysis can be applied to moderate size Simulink models of continuous and hybrid systems.

The complexity of present day embedded systems (continuous processes controlled by digital processors), and the increased demands on their reliability motivate the need for monitoring and fault isolation capabilities in the embedded processors. This paper develops monitoring, prediction, and fault isolation methods for abrupt faults in complex dynamic systems. The transient behavior in response to these faults is analyzed in a qualitative framework using parsimonious topological system models. Predicted transient effects of hypothesized faults are captured in the form of signatures that specify future faulty behavior as higher order time-derivatives. The dynamic effects of faults are analyzed by a progressive monitoring scheme till transient analysis mechanisms have to be suspended in favor of steady state analysis. This methodology has been successfully applied to monitoring of the secondary sodium cooling loop of a fast breeder reactor.