TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers

Neural network (NN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks achieve state-of-the-art performance in feed-forward NNs and image recognition tasks, they do not perform as well on time series classification with recurrent neural network (RNN) models. This is because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity of time series data to perturbations challenges the traditional local optimization objective of the adversarial attack. In this paper, a black-box method called TSFool is proposed to efficiently craft highly-imperceptible adversarial time series for RNN classifiers. We propose a novel global optimization objective named Camouflage Coefficient to consider the imperceptibility of adversarial samples from the perspective of class distribution, and accordingly refine the adversarial attack as a multi-objective optimization problem to enhance the perturbation quality. To get rid of the dependence on gradient information, we also propose a new idea that introduces a representation model for RNN to capture deeply embedded vulnerable samples having otherness between their features and latent manifold, based on which the optimization solution can be heuristically approximated. Experiments on 10 UCR datasets are conducted to confirm that TSFool averagely outperforms existing methods with a 46.3% higher attack success rate, 87.4% smaller perturbation and 25.6% better Camouflage Coefficient at a similar time cost.Comment: 9 pages, 7 figure

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

MARTE/pCCSL: Modeling and Refining Stochastic Behaviors of CPSs with Probabilistic Logical Clocks

Best Paper AwardInternational audienceCyber-Physical Systems (CPSs) are networks of heterogeneous embedded systems immersed within a physical environment. Several ad-hoc frameworks and mathematical models have been studied to deal with challenging issues raised by CPSs. In this paper, we explore a more standard-based approach that relies on SysML/MARTE to capture different aspects of CPSs, including structure, behaviors, clock constraints, and non-functional properties. The novelty of our work lies in the use of logical clocks and MARTE/CCSL to drive and coordinate different models. Meanwhile, to capture stochastic behaviors of CPSs, we propose an extension of CCSL, called pCCSL, where logical clocks are adorned with stochastic properties. Possible variants are explored using Statistical Model Checking (SMC) via a transformation from the MARTE/pCCSL models into Stochastic Hybrid Automata. The whole process is illustrated through a case study of energy-aware building, in which the system is modeled by SysML/MARTE/pCCSL and different variants are explored through SMC to help expose the best alternative solutions

Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

xSHS: An Executable Domain-Specific Modeling Language for Modeling Stochastic and Hybrid Behaviors of Cyber-Physical Systems

International audienceCyber-Physical Systems (CPS) integrate discrete computational processes and continuous physical ones in a feedback loop. Design and analysis of CPS become difficult since their dynamic behaviors rely on heterogeneous descriptions from many fields. Domain-Specific Modeling Language (DSML) offers an effective and tailor-made solution for focusing on a specific field. However, to address CPS we need to bring together several DSMLs in a coordinated sensible way. The GEMOC Studio is meant to be an integration platform for putting together several DSMLs. This paper relies on it and brings a new DSML, called xSHS (for Executable Stochastic Hybrid Statechart), into the focus. It aims at modeling the stochastic and hybrid behaviors of CPS. We discuss here the abstract syntax, a proposed concrete syntax and an operational semantics that makes the language executable. We exploit both the language and modeling workbenches of the GEMOC Studio and we provide a simulation engine that implements the operational semantics. A temperature control system is used as a case study

pCSSL: A stochastic extension to MARTE/CCSL for modeling uncertainty in Cyber Physical Systems

International audienceCyber-Physical Systems (CPSs) are networks of heterogeneous embedded systems immersed within a physical environment, thus combining discrete and continuous processes. As for any complex systems, the global system behavior is difficult to predict, in an analytical way, from the individual behaviors of its parts. A global analysis can only be done through a holistic process, via simulation for instance, requiring precise models of the parts and of their interactions. While the subsystems are usually expected to be fully deterministic, their interactions with the uncertain environment can be difficult to characterize precisely. We propose an approach to characterize the environment and its interactions through stochastic properties, while the discrete part remains fully determined. The novelty of our work is that we explore a more standard-based approach relying on SysML/MARTE. CCSL and logical clocks are used to identify synchronization points in the various heterogeneous UML diagrams. A CCSL specification expresses a set of possible behaviors. Refinement is performed by adding new constraints and thus reducing the set of possible behaviors. The classical MARTE/CCSL-based process explores the remaining solutions through simulation by applying a simulation policy. To help exploring the solution state-space, we propose a stochastic extension of CCSL, called pCCSL, to characterize the likelihood of different configurations to occur. Then, we use Statistical Model Checking to explore alternative solutions and drive the refinement process. We illustrate our proposition by modeling an energy-aware building, with different control strategies and occupant energy usage models. We explore the impact on the energy footprint of the different variants and control strategies

Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes