In this paper, we propose an online federated learning framework with massive random access, aiming to learn a sequence of global models using local data that are sequentially collected by massive edge devices. As onl...
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Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...
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Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
In this paper, we have proposed testing the graphical user interface (GUI) applications using the idea of finite state machines and then comparing the results with the model generated using Matlab: which represents th...
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Characterizing the susceptibility of an IC while it is integrated within a system can be challenging. Characterization is even harder if one wants to know the waveform at the target IC pin when injecting a signal on t...
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Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder's complexity, leading to a focus on mul...
Security and authentication of low-power embedded systems for the internet of things (IoT) is a research topic that is growing due to the extensive numbers of devices. Modern biomedical devices configured and calibrat...
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The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affec...
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The purpose of virtual network embedding (VNE) is to embed nodes and links of virtual networks to nodes and links of a physical network effectively and efficiently. In other word, it specifies how to efficiently use n...
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Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity ...
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In this paper, the impact of data pre-processing strategies on the performance of forecasting models for solar power generation is comprehensively analyzed. The integration of outlier detection, normalization, and mis...
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