Browsing by Subject "Sequential data"

Now showing 1 - 2 of 2

Open Access
Collusion-secure watermarking for sequential data
(2017-09) Yılmaz, Arif
In this work, we address the liability issues that may arise due to unauthorized sharing of personal data. We consider a scenario in which an individual shares his sequential data (such as genomic data or location patterns) with several service providers (SPs). In such a scenario, if his data is shared with other third parties without his consent, the individual wants to determine the service provider that is responsible for this unauthorized sharing. To provide this functionality, we propose a novel optimization-based watermarking scheme for sharing of sequential data. Thus, in the case of an unauthorized sharing of sensitive data, the proposed scheme can nd the source of the leakage by checking the watermark inside the leaked data. In particular, the proposed schemes guarantees with a high probability that (i) the SP that receives the data cannot understand the watermarked data points, (ii) when more than one SPs aggregate their data, they still cannot determine the watermarked data points, (iii) even if the unauthorized sharing involves only a portion of the original data, the corresponding SP can be kept responsible for the leakage, and (iv) the added watermark is compliant with the nature of the corresponding data. That is, if there are inherent correlations in the data, the added watermark still preserves such correlations. Watermarking typically means changing certain parts of the data, and hence it may have negative e ects on data utility. The proposed scheme also minimizes such utility loss while it provides the aforementioned security guarantees. Furthermore, we conduct a case study of the proposed scheme on genomic data and show the security and utility guarantees of the proposed scheme.
Open Access
Computer network intrusion detection using sequential LSTM neural networks autoencoders
(IEEE, 2018-05) Mirza, Ali H.; Coşan, Selin
In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.