The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital in...
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ISBN:
(纸本)9798331518097
The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the
The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers pat...
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Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometime...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.
The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
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Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective ...
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作者：
Ramasamy, G.Kamalakkannan, S.Vels Institute of Science
Technology & Advanced Studies Department of Computer Science School of Computing Sciences Pallavaram Chennai India Vels Institute of Science
Technology & Advanced Studies Department of Information Technology School of Computing Sciences Pallavaram Chennai India
Container-based virtualisation has become a cornerstone of Cloud computing (CC) due to its lightweight and scalable properties when compared to traditional virtual machines. However, it is still difficult to optimisin...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the ef...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.
Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil let...
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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Prod...
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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited ***-GRU captures both spatial and sequential dependencies in user-item *** innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant *** approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item *** model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional *** study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
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