International Conference Papers

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Start date: 2020Subject: search.filters.subject.Deep learning

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    Investigating Security and Privacy Concerns in Deep-Learning-based Pervasive Health Monitoring Architectures
    (IEEE, 2023-11) Boulemtafes, Amine; Amira, Abdelouahab; Derki, Mohamed Saddek; Hadjar, Samir
    Pervasive Health Monitoring (PHM) uses sensors and wearable devices and data analytics for real-time health monitoring. It enables early detection and personalized care interventions. This technology has the potential to revolutionize healthcare by improving proactive and preventive care.Besides, Deep learning (DL) based PHM is even more promising as it improves the discovery of complex patterns and correlations. This leads to precise health monitoring and personalized care, enhances diagnostics, and ultimately improves patient outcomes in the field of healthcare.However, privacy and security considerations must be addressed for successful implementation. This paper investigates the security and privacy concerns in Pervasive Health Monitoring architectures. It discusses through an illustrative DL-based PHM architecture the potential threats and attacks during the inference and training phases, and identifies key security and privacy issues. It also gives insights on countermeasures and technological solutions that can address security and privacy concerns in PHM architectures.
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    DIAG a diagnostic web application based on lung CT Scan images and deep learning
    (IOS Press Ebooks, 2021-05-29) Hadj Bouzid, Amel Imene; Yahiaoui, Saïd; Lounis, Anis; Berrani, Sid-Ahmed; Belbachir, Hacène; Naili, Qaid; Abdi, Mohamed El Hafedh; Bensalah, Kawthar; Belazzougui, Djamal
    Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.
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    Side Channel Attack using Machine Learning
    (IEEE, 2022-12-15) Amrouche, Amina; Boubchir, Larbi; Yahiaoui, Saïd
    The overwhelming majority of significant security threats are hardware-based, where the attackers attempt to steal information straight from the hardware that our secure and encrypted software operates on. Unquestionably, side-channel attacks are one of the most severe risks to hardware security. Rather than depending on bugs in the program itself, a side-channel attack exploits information leaked from the program implementation in order to exfiltrate sensitive secret information such as cryptographic keys. A side channel assault could manifest in different ways including electromagnetic radiation, power consumption, timing data, or even acoustic emanation. Ever since the side-channel attacks were introduced in the 1990s, a number of significant attacks on cryptographic implementations utilizing side-channel analysis have emerged, such as template attacks, and attacks based on power analysis and electromagnetic analysis. However, Artificial Intelligence has become more prevalent. Attackers are now more interested in machine learning and deep learning technologies that enable them to interpret the extracted raw data. The aim of this paper is to highlight the main methods of machine learning and deep learning that are used in the most recent studies that deal with different types of side-channel attacks.