Mohammad Ali Jamshidi; Mohammad Mahdi Mojahedian; Mohammad Reza Aref
Abstract
To enhance the accuracy of learning models, it becomes imperative to train them on more extensive datasets. Unfortunately, access to such data is often restricted because data providers are hesitant to share their data due to privacy concerns. Hence, it ...
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To enhance the accuracy of learning models, it becomes imperative to train them on more extensive datasets. Unfortunately, access to such data is often restricted because data providers are hesitant to share their data due to privacy concerns. Hence, it is critical to develop obfuscation techniques that empower data providers to transform their datasets into new ones that ensure the desired level of privacy. In this paper, we present an approach where data providers utilize a neural network based on the autoencoder architecture to safeguard the sensitive components of their data while preserving the utility of the remaining parts. More specifically, within the autoencoder framework and after the encoding process, a classifier is used to extract the private feature from the dataset. This feature is then decorrelated from the other remaining features and subsequently subjected to noise. The proposed method is flexible, allowing data providers to adjust their desired level of privacy by changing the noise level. Additionally, our approach demonstrates superior performance in achieving the desired trade-off between utility and privacy compared to similar methods, all while maintaining a simpler structure.
Amirhosein Salehi; Siavash Ahmadi; Mohammad Reza Aref
Abstract
Industrial control systems are widely used in industrial sectors and critical infrastructures to monitor and control industrial processes. Recently, the security of industrial control systems has attracted a lot of attention, because these systems are now increasingly interacting with the Internet. Classic ...
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Industrial control systems are widely used in industrial sectors and critical infrastructures to monitor and control industrial processes. Recently, the security of industrial control systems has attracted a lot of attention, because these systems are now increasingly interacting with the Internet. Classic systems are suffering from many security problems and with the expansionof Internet connectivity, they are now exposed to new types of threats and cyber-attacks. Addressing this, intrusion detection technology is one of the most important security solutions that is used in industrial control systems to identifypotential attacks and malicious activities. In this paper, we propose Stacked Autoencoder-Deep Neural Network (SAE-DNN), as a semi-supervised Intrusion Detection System (IDS) with appropriate performance and applicability on a wide range of Cyber-Physical Systems (CPSs). The proposed approach comprises a stacked autoencoder, a deep learning-based feature extractor, helping us with a low dimension and low noise representation of data. In addition, our system includes a deep neural network (DNN)-based classifier, which is used to detect anomalies with a high detection rate and low false positive rate in a real-time process. The SAE-DNN’s performance is evaluated on the WADI dataset, which is a real testbed for a water distribution system. The results indicate the superior performance of our approach over existing supervised and unsupervised methods while using a few percentages of labeled data.