In this paper,we propose a game theory framework to solve advanced persistent threat problems,especially considering two types of insider threats:malicious and *** this framework,we establish a unified three-player ga...
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In this paper,we propose a game theory framework to solve advanced persistent threat problems,especially considering two types of insider threats:malicious and *** this framework,we establish a unified three-player game model and derive Nash equilibria in response to different types of insider *** analyzing these Nash equilibria,we provide quantitative solutions to advanced persistent threat problems pertaining to insider ***,we have conducted a comparative assessment of the optimal defense strategy and corresponding defender's costs between two types of insider ***,our findings advocate a more proactive defense strategy against inadvertent insider threats in contrast to malicious ones,despite the latter imposing a higher burden on the *** theoretical results are substantiated by numerical results,which additionally include a detailed exploration of the conditions under which different insiders adopt risky *** conditions can serve as guiding indicators for the defender when calibrating their monitoring intensities and devising defensive strategies.
This research addresses the critical challenge of ransomware detection through the use of deep learning and machine learning methods. Because ransomware is a serious threat to cybersecurity, it is imperative that adva...
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The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage t...
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The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.
The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficientl...
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The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to tran...
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The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.
Securing healthcare deployments has become one of the primary concerns for Internet-of-Medical-Things (IoMT) designers. This is due to the fact that IoMT deployments are under constant attacks from both internal &...
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Securing healthcare deployments has become one of the primary concerns for Internet-of-Medical-Things (IoMT) designers. This is due to the fact that IoMT deployments are under constant attacks from both internal & external adversaries. To incorporate attack detection, researchers have observed that blockchain-based deployments are highly efficient, due to their immutability, traceability, distributed computing & transparency characteristics. But single-chained deployments cannot be scaled due to an exponential increase in computational delays. Moreover, processing efficiency of secure IoMT-based data must be enhanced, which will assist in adding robustness to these deployments. This paper suggests creating a highly secure IoMT data processing model powered by deep transfer learning and blockchain that can be applied to multimodal healthcare installations in order to combine these features. The suggested framework optimizes blockchain write & read delays via incorporation of a Proof-of-Medical-Trust (PoMT) consensus, that is capable of distributed deployment and has inbuilt sidechain support for multichain use cases. The PoMT Model uses a Genetic Chain Optimization (GCO) method, that assists in segregating single chained data into sharded chains. Data is securely stored on these sharded chains, and is processed via a deep transfer learning (DTL) based model that fuses Gated Recurrent Unit (GRU) with Long-Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs) for classification of underlying data into different disease conditions. The classified data is further processed via a customized 2D Convolutional Neural Network (2D CNN), that helps with recognition of disease severity and progression levels via augmented analysis. The model was tested under Sybil, Spoofing, & Spying attack types, and was observed to be 10.4% faster in terms of mining performance, 6.5% faster in terms of reading performance, and showcased 9.3% better energy efficiency when compared w.r.
Effective communication in vehicular networks is essential for realizing the potential benefits of intelligent transportation systems, including improved safety, reduced congestion, and enhanced overall traffic manage...
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With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It...
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Facial Expression Recognition (FER) is a vital area of research in affective computing, permitting systems to automatically interpret and respond to human emotions by studying and analyzing facial expressions. FER inv...
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Satellite image analysis is revolutionizing fields like urban planning, environmental conservation, and disaster management, but achieving high-precision object detection remains a challenge. Among deep learning archi...
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