The platooning of mobile robots, facilitated by Device-to-Device (D2D) communications, has become central in Industry 4.0, enhancing material transport, reducing energy consumption, and improving safety in smart facto...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have be...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental h...
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This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial ev...
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The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern gener...
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Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving ...
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Federated Learning (FL) has emerged as a promising approach to address the challenges of data privacy, security, and scalability in Internet of Things (IoT) environments. This paper provides a comprehensive survey of ...
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Container technology represents a dynamic and adaptable solution in the field of software development for cloud computing applications and services, presenting the advantages of both portability and operational effici...
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Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of cost...
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Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of costly imaging and invasive biopsies, are of high burdens. Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. Specifically, we present a new Optimized InceptionResNetV2 model significantly optimized through the thoughtful addition of large data augmentation to increase robustness, LeakyReLU activation to facilitate gradient flow and accelerate learning, and MeanDropout regularization to mitigate overfitting and improve generalization. The model was also trained using Quantization Aware Training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. The performance on our proposed approach for the massive mammogram dataset reflects an evident improvement in detection performance over traditional techniques. Our InceptionResNetV2 optimized achieved state-of-the-art accuracy with outstanding measures of 98.06% sensitivity, 97.05%, positive predictive value (PPV) and specificity of 99.60%, negative predictive value (NPV) of 86.83%, 97.94% accuracy, F1-score of 96.90%, Matthews correlation coefficient (MCC) of 90.67%, and AUC of 0.9939. The benefits of proposed system are that it can deliver a more efficient, precise, and possibly cost-effective diagnostic tool for breast cancer. Through synergistic integration of architectural optimization, sophisticated regularization methods, and deployment-aware training, our proposed system enables earlier
Self-healing group key distribution (SGKD) protocols guarantee the security of group communications by allowing authorized users to independently recover missed previous session keys from the current broadcast without...
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