As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely ...
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Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely on deep learning algorithms, but these algorithms suffer from complexity in implementation, the need for high processing power, and high memory consumption. In addition to security issues, firstly, the number of datasets that are based on SDN protocols are very small. Secondly, the ones that are available encompass a variety of attacks in the network and do not focus on a single attack. For this reason, to introduce an SDN-based IDS with a focus on Distributed Denial of Service (DDoS) attacks, it is necessary to generate a DDoS-oriented dataset whose features can train a high-quality IDS. In this work, in order to address two important challenges in SDNs, in the first step, we generate three DDoS attack datasets based on three common and different network topologies. Then, in the second step, using the Convolutional Tsetlin Machine (CTM) algorithm, we introduce a lightweight IDS for DDoS attack dubbed "CTMBIDS," with which we implement an anomaly-based IDS. The lightweight nature of the CTMBIDS stems from its low memory consumption and also its interpretability compared to the existing complex deep learning models. The low usage of system resources for the CTMBIDS makes it an ideal choice for an optimal software that consumes the SDN controller’s least amount of memory. Also, in order to ascertain the quality of the generated datasets, we compare the empirical results of our work with the DDoS attacks of the KDDCup99 benchmark dataset as well. Since the main focus of this work is on a lightweight IDS, the results of this work show that the CTMBIDS performs much more efficiently than traditional and deep learning based machine learning algorithms. Furthermore, the results also show that in most datasets, the proposed method has relatively equal or better
The detection of community structures in complex networks has garnered significant attention in recent years. Given its NP-hardness, numerous evolutionary optimization-based approaches have been proposed. However, the...
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Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as...
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In recent years, convolutional neural networks have significantly advanced the field of computer vision by automatically extracting features from image data. CNNs enable the modeling of complex and abstract image feat...
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Skin cancer is acknowledged as the most prevalent form of cancer on a global scale. Failure to detect it in its initial phases can lead to fatality, underscoring the significance of early diagnosis. While visible to t...
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In this paper, we investigate the influence of various factors, such as programming language, testing environment, and input data, on the accuracy of algorithm execution measurements. To conduct this study, we used th...
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Flux-barrier permanent magnet Vernier motors are among the latest magnetic motors developed and optimized in the industry. In these motors, the arrangement of magnets plays a critical role and significantly impacts on...
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This paper proposes a deep-learning color image steganography scheme that employs convolutional autoencoders with ResNet architecture. In the proposed method, all images are passed through the Preproccessing model whi...
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