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.
Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumo...
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Social networks empower individuals to freely share their perspectives on a diverse array of subjects. One such topic is the impact of the coronavirus vaccine in preventing the disease. People have written their varyi...
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This paper, therefore, proposes the application of the VAEs in enhancing the coherence and the alignment of the multiple track music AI-sync'd above. In our case, we want to take advantage of the feature of VAEs i...
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The persistently high incidence of breast cancer emphasizes the need for precise detection in its ***-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and ef...
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The persistently high incidence of breast cancer emphasizes the need for precise detection in its ***-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical *** Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward *** when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine *** enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME *** original RIME initially conducts an efficient optimization to target promising *** double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local *** contrast,the crisscross operations perform a further local exploration of the generated feasible *** performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark ***,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology *** results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
As cloud data centres expand and provide more services, they consume more energy and cause challenges for the environment. To address this, there is a focus on energy-saving scheduling approaches in cloud computing. T...
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Cloud Computing (CC) generally exhibits varying workload patterns. This autoscaling feature of CC has been extensively managed through predictive cloud resource management approaches. For this reason, a solitary forec...
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The new technology, with the aid of newly emerging knowledge known as cloud computing, can provide resources remotely and on demand. With the use of cloud computing, users can operate in settings where they are not de...
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It is well-known that Information Security Risk Management (ISRM) activities can be challenging to perform and that tool support could provide support in different ways, for example, by automating tasks, guiding the u...
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Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of hete...
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Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
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