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Abstract

Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

. Kiani, “Computerized screening of children congenital heart diseases”, Computer methods programs in biomedicine 92 (2) (2008) 186-192. [30] Z. Abduh, E. A. Nehary, M. A. Wahed, and Y. M. Kadah, “Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coeficients stacked autoencoder deep neural network”, Journal of Medical Imaging Health Informatics 9 (1) (2019) 1-8. [31] S. E. Schmidt, C. Holst-Hansen, C. Graff, E. Toft, and J. J. Struijk, “Segmentation of heart sound recordings by a duration-dependent hidden Markov model

Abstract

We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

Abstract

An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

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Soft Computing Research 9, 1 (2019), 21–40. [25] Huang, G., Liu, Z., v. d. Maaten, L., and Weinberger, K. Q.; Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017), pp. 2261–2269. [26] II, A. G. O., Giles, C. L., and Reitter, D.; Online semi-supervised learning with deep hybrid boltzmann machines and denoising autoencoders. CoRR abs/1511.06964 (2015). [27] Jaworski, M., Duda, P., and Rutkowski, L.; On applying the Restricted Boltzmann Machine to active concept drift detection. In Proceedings of

, D. Nguyen-Doan, M. Robnik-Sikonja, D. Zaharie, Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders, in: E-Health and Bioengineering Conference (EHB) (2019) ⇒109 [11] T. Nyíri, A. Kiss, Novel Ensembling Methods for Dermatological Image Classification, in: International Conference on Theory and Practice of Natural Computing (2018) ⇒109 [12] N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, A. Swami, Practical black-box attacks against machine learning, in: Proceedings of the 2017 ACM on Asia conference on computer and

Training by Reducing Internal Covariate Shift", 2015. 15. Kaminski, B., Jakubczyk, M., and Szufel, P., "A framework for sensitivity analysis of decision trees". Central European Journal of Operations Research, 2017. 16. Moro, S., Cortez, P. and Rita, P., “A Data-Driven Approach to Predict the Success of Bank Telemarketing”. Decision Support Systems, Vol. 62, pp22-31, 2014. 17. Liou, C.-Y., Cheng, W,-C., Liou, J.-W., and Liou, D.-R., "Autoencoder for words", Neurocomputing, 139, 2014. 18. McLachlan, G. J., “Discriminant Analysis and Statistical Pattern Recognition”. Wiley

connections, arXiv: 1710.05268. Fabisch, A. and Metzen, J.H. (2014). Active contextual policy search, Journal of Machine Learning Research 15 (1): 3371–3399. Finn, C. and Levine, S. (2016). Deep visual foresight for planning robot motion, arXiv: 1610.00696. Finn, C., Tan, X.Y., Duan, Y., Darrell, T., Levine, S. and Abbeel, P. (2015). Deep spatial autoencoders for visuomotor learning, arXiv: 1509.06113. Gruslys, A., Azar, M.G., Bellemare, M.G. and Munos, R. (2017). The reactor: A sample-efficient actor-critic architecture, arXiv: 1704.04651. Hayes, G. and Demiris, J