The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core...
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The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrat...
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The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.
Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures ...
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ISBN:
(数字)9798350384475
ISBN:
(纸本)9798350384482
Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures do not systematically consider restrictions and performance requirements inherent to the edge, such as system responsiveness and resource consumption. In this paper, we attempt to investigate the performance of change-point based detectors, i.e., a class of lightweight and accurate AD methods, in relation to the requirements of edge cloud systems. Firstly, we review the theoretical properties of two major categories of change point approaches, i.e., Bayesian and cumulative sum (CUSUM), also discussing their suitability for edge systems. Secondly, we introduce a novel experimental methodology and apply it over two distinct edge cloud test-beds to evaluate the performance of such mechanisms in real-world edge environments. Our experimental results provide important insights and trade-offs for the applicability and the online performance of the selected change point detectors.
Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures ...
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Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensembl...
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There is an increasing demand for transmission system operators to expand their participation in the electricity reserve markets, encompassing smaller businesses and house-holds that are currently excluded due to mark...
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ISBN:
(数字)9798350377491
ISBN:
(纸本)9798350377507
There is an increasing demand for transmission system operators to expand their participation in the electricity reserve markets, encompassing smaller businesses and house-holds that are currently excluded due to market constraints. These smaller entities require an aggregator's assistance to pool the loads of several minor actors, enabling their participation in the markets through an aggregator that can optimize bids. This paper introduces a matheuristic framework aimed at maximizing revenue in the Norwegian power reserve markets, showcasing the potential effectiveness of aggregators. The framework is utilized to analyze the optimal bidding strategies for an aggregator within the Norwegian Reserve Markets over two distinct time periods, using real market prices, flexible power capacity, and customer consumption data. The heuristics are evaluated and the outcomes are compared with those of a mathematical model. The matheuristic models achieved results ranging from 88–94 % relative to the corresponding mathematical model.
A paragraph raises pertinent issues about linguistic disparities in understanding the semantic relationships between sentences within the natural language processing domain (NLP) Addressing this challenge necessitates...
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In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is...
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In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.
Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperabi...
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Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows
This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwardi...
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