This paper investigates dynamic anomaly detection in resource-constrained environments by leveraging Robust Random Cut Forests (RRCF). Anomaly detection is crucial for maintaining the integrity and security of data st...
详细信息
This paper investigates dynamic anomaly detection in resource-constrained environments by leveraging Robust Random Cut Forests (RRCF). Anomaly detection is crucial for maintaining the integrity and security of data streams in Internet of Things (IoT) environments, where data is continuously generated and often subject to noise and fluctuations. We begin with a comprehensive exploration of resilient random cut data structures tailored for analyzing incoming data streams, highlighting their effectiveness in adapting to the dynamic nature of *** methodology encompasses extensive experimentation with diverse datasets, including real-time Arduino data and benchmark datasets such as IoT-23 and CIC-IoT. Through this approach, we assess the performance of the RRCF algorithm under various scenarios, focusing on its capability to accurately identify trends and anomalies over time. Notably, we achieve significant performance improvements, with an average Area Under the Curve (AUC) of 95.6 and an F1 score of 0.86, demonstrating RRCF’s effectiveness in real-time anomaly *** further enhance detection accuracy, we introduce dynamic thresholds that adapt to changing data characteristics, allowing our model to maintain robust performance even in the presence of noise. Detailed evaluations reveal that our approach consistently outperforms existing state-of-the-art methods, particularly in terms of handling noisy data and ensuring computational efficiency under resource *** findings underscore the potential of RRCF as a powerful tool for real-time applications within IoT systems, providing a solid theoretical foundation for future advancements in dynamic anomaly detection. By investigating non-parametric anomalies and analyzing the influence of external factors on data integrity, we uncover hidden patterns amidst dynamic fluctuations. This research emphasizes the need for adaptive strategies in evolving data landscapes, laying the groundwork for enhanced resil
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...
详细信息
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.
Delineating the boundaries of the optic disc and cup regions is a critical pre-requisite for glaucoma screening because it allows for precise measurement of key parameters, such as cup-to-disc ratio, which is a critic...
详细信息
The growing prevalence of Internet of Things (IoT) devices has heightened vulnerabilities to botnet-based cyberattacks, necessitating robust detection mechanisms. This paper proposes DenseRSE-ASPPNet, an advanced deep...
详细信息
The emergence of interconnected UAVs has given rise to the creation of flying ad hoc networks (FANETs) aimed at efficiently facilitating network-dependent services. However, FANET encountered considerable challenges i...
详细信息
A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of ...
详细信息
Pancreatic cancer's devastating impact and low survival rates call for improved detection methods. While Artificial Intelligence has shown remarkable progress, its increasing complexity has led to "black box&...
详细信息
Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our s...
详细信息
At present, recommendation systems have become pivotal in personalized education learning management systems, where there is a growing need for location-based suggestions. Our problem addresses the inefficiency of cur...
详细信息
Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ...
详细信息
Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ***,a temporary CDN without an existing infrastructure is *** achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching *** the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the *** present three content distribution algorithms that consider the constraints and mobility of *** main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of *** proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through ***,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.
暂无评论