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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This paper proposed a model based on bidirectional Long Short-Term Memory (Bi-LSTM) and Bayesian optimization to detect different drones in different Scenarios. Six different drones in three distinct scenarios—cloudy...
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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 ...
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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
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.
A d-regular graph X is called d-rainbow domination regular or d-RDR, if its d-rainbow domination number γrd(X) attains the lower bound n/2 for d-regular graphs, where n is the number of vertices. In the paper, two co...
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Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to add...
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With the increasing usage of cloud computing in many fields, concerns about the secrecy of data storage in the cloud have been growing. Many types of data are stored in cloud computing, such as text, images, audio, vi...
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This paper explores the adoption of cloud computing and big data technologies in small and medium enterprises (SMEs), focusing on synthesizing key factors influencing adoption. By conducting a comprehensive literature...
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This paper explores the adoption of cloud computing and big data technologies in small and medium enterprises (SMEs), focusing on synthesizing key factors influencing adoption. By conducting a comprehensive literature review and analyzing theoretical frameworks such as the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, this study proposes a novel conceptual model tailored to the unique socio-economic context of SMEs in the MENA region. The methodology includes an analysis of 30 research articles, highlighting enablers and barriers to adoption. Key contributions include a taxonomy of factors and actionable insights for policymakers and practitioners. This research addresses gaps in existing studies by providing a region-specific perspective on cloud computing and big data technologies adoption for SMEs. This strong tendency towards services provided by cloud computing is very clear, and to shape their future IT, it's worth highlighting the existence of such technology. A synthesis of the literature on cloud computing adoption was presented in this paper classifying reviews based on factors that play an important role in taking the adoption decision incorporating the theoretical frameworks. Moreover, the relationship between big data and cloud computing has been explored. In addition to proposing a synthesized findings model, this paper includes various factors derived from the literature and related theoretical frameworks. In this paper we select research articles from broadly recognized research databases, as well as undertaking a comprehensive review of these selected articles focusing on the adoption of cloud computing and big data. It also includes a bibliography containing the most relevant publications in these domains, from 2011 to 2024. By examining 30 articles, the objectives of the paper are to analyze data derived from articles as well as to study the academic frameworks used in both developing and developed
Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD di...
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Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class *** study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature *** study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and *** proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD *** ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
Efficient task scheduling and resource allocation are essential for optimizing performance in cloud computing environments. The presence of priority constraints necessitates advanced solutions capable of addressing th...
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