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The data generated by millions of sensors in Industrial Internet of Things (IIoT) is extremely dynamic, heterogeneous, and large scale. It poses great challenges on the real-time analysis and decision making for anomaly detection in IIoT. In this paper, we propose a LSTM-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in IIoT. In a nutshell, the LSTM-NN builds model on normal time series. It detects outliers by utilising the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on 3 real-life datasets that involve both long-term and short-term time-dependency. Empirical studies demonstrate that our proposed techniques outperform the bestknown competitors, which is a preferable choice for detecting anomalies.

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Nowadays, internet is becoming a suitable way of accessing the databases. Such data are exposed to various types of attack with the aim to confuse the ownership proofing or the content protection. In this paper, we propose a new approach based on fragile zero watermarking for the authentication of numeric relational data. Contrary to some previous databases watermarking techniques which cause some distortions in the original database and may not preserve the data usability constraints, our approach simply seeks to generate the watermark from the original database. First, the adopted method partitions the database relation into independent square matrix groups. Then, group-based watermarks are securely generated and registered in a trusted third party. The integrity verification is performed by computing the determinant and the diagonal’s minor for each group. As a result, tampering can be localized up to attribute group level. Theoretical and experimental results demonstrate that the proposed technique is resilient against tuples insertion, tuples deletion, and attributes values modification attacks. Furthermore, comparison with recent related effort shows that our scheme performs better in detecting multifaceted attacks.

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