Deep learning based models are vulnerable to adversarial attacks. These attacks can be much more harmful in case of targeted attacks, where an attacker tries not only to fool the deep learning model, but also to misguide the model to predict a specific class. Such targeted and untargeted attacks are specifically tailored for an individual sample and require addition of an imperceptible noise to the sample. In contrast, universal adversarial attack calculates a special imperceptible noise which can be added to any sample of the given dataset so that, the deep learning model is forced to predict a wrong class. To the best of our knowledge these targeted and universal attacks on time series data have not been studied in any of the previous works. In this work, we have performed untargeted, targeted and universal adversarial attacks on UCR time series datasets. Our results show that deep learning based time series classification models are vulnerable to these attacks. We also show that universal adversarial attacks have good generalization property as it need only a fraction of the training data. We have also performed adversarial training based adversarial defense. Our results show that models trained adversarially using Fast gradient sign method (FGSM), a single step attack, are able to defend against FGSM as well as Basic iterative method (BIM), a popular iterative attack.

Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems.

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