The inability to capture the temporal dynamics of network interactions limits traditional intrusion detection systems (IDSs) in detecting sophisticated threats that evolve over time. This research introduces DynKDD, a...
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The primary objective of this research is to develop and promote efficient and secure de-identification technology to address the application of sensitive personal information in healthcare big data. Considering the o...
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This systematic literature review (SLR) presents a comprehensive overview of the current state of interpretability techniques in malware classification based on adversarial defense mechanisms, challenges, and future d...
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ISBN:
(数字)9798331522667
ISBN:
(纸本)9798331522674
This systematic literature review (SLR) presents a comprehensive overview of the current state of interpretability techniques in malware classification based on adversarial defense mechanisms, challenges, and future directions. Several ML and DL approaches have been integrated into the malware detection and classification defense systems, where adversarial attacks represent a crucial security threat against these defense systems. The interpretability techniques have been found very important to be applied to enhance the performance of these security systems and develop more explainable models and decisions for users and security experts. The SLR was conducted by combining and reviewing scientific literature on adversarial defense mechanisms-based interpretability techniques in the field of malware classification published between 2013 and 2024. This SLR presents a new insight into the categorization of interpretability techniques' implications in adversarial defense systems. We evaluate the importance and effectiveness of interpretability techniques by investigating ML models, adversarial attacks, evaluation methods, datasets, user trust, and malware features. Furthermore, the study provides challenges and future research directions for further existing issues
Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solutio...
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Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** ...
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Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.
Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing ...
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The proliferated smart TV market has sparked a race among the tech giants to capture market share, with Google aggressively pursuing this domain through partnerships with third-party smart TV manufacturers. However, t...
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In this paper, we explore the exponential growth of geometric structures starting from a single node, focusing on centralized growth operations. We identify a parameter k, representing the number of turning points wit...
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This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cu...
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This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that consider gender, r...
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