Totalisation of operations and functions is a subject of great importance in the precise sciences at large, and in particular, in computer science, where computed functions must be totalised to account for edge cases ...
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Totalisation of operations and functions is a subject of great importance in the precise sciences at large, and in particular, in computer science, where computed functions must be totalised to account for edge cases and prevent system failures. In this paper, we investigate the totalisation of computable functions in the general and abstract setting of the axiomatic theory of computing systems, such as automata, computernetworks, and algorithms, allowing us to define characteristics and discover relations which resonate true through many computational models - from individual automata through complex distributedsystems with concurrent functioning. We explore the possible types of totalisations, the complexes of computing systems that do (or do not) have totalisations, and the behaviour of totalisation in various transformations and compositions. In addition, we point to a few interesting directions for future research.
Software-defined networking (SDN) has emerged as a promising approach for managing network infrastructure through a centralized controller. However, the dynamic nature of SDN makes it susceptible to security threats, ...
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Software-defined networking (SDN) has emerged as a promising approach for managing network infrastructure through a centralized controller. However, the dynamic nature of SDN makes it susceptible to security threats, including DoS and DDoS attacks. Intrusion detection systems (IDS) based on machine learning (ML) can efficiently detect and mitigate these attacks. This study compares two ML models, namely support vector machines (SVM) and artificial neural networks (ANN), for intelligent intrusion detection in SDN. To assess the performance of the ML models, we utilized the NSL-KDD dataset, with a specific emphasis on DDoS attacks, and compared their accuracy (Acc), precision, recall, and F1-score metrics. The implementation outcomes show that SVM is better than ANN regarding response time and Acc. (c) 2023 The Authors. Published by Elsevier B.V.
Data transfer efficiency is a frequent bottleneck of distributed (co-)simulations and X-in-the-loop systems. One of the key reasons, particularly in Agent-Based Simulation (ABS), is related to the low serialization pe...
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The cybersecurity field has witnessed several Intrusion Detection System (IDS) and Anomaly Detection, which are essential for identifying malicious activities in network traffic. However, growing volume and diversity ...
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Efficient super-resolution networks have been more well-known in recent years due to their effectiveness in increasing image resolution with the least amount of processing cost. This research introduces a novel lightw...
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The Streamlined Power Integrity Model (SPIM) has received approval from the IBIS Open Forum. After a comprehensive overview of the structure, SPIM is elaborated with its generation, correlation, validation, and practi...
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In this paper, a distributed algorithm is presented that localizes large number of sensors in localizable wireless sensor networks. It is well known that a network is localizable if and only if the underlying graph is...
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The amount of data being exchanged over the internet is enormous. Attackers are finding novel ways to evade rules, investigate network defenses, and launch successful attacks. Intrusion detection is one of the effecti...
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The amount of data being exchanged over the internet is enormous. Attackers are finding novel ways to evade rules, investigate network defenses, and launch successful attacks. Intrusion detection is one of the effective means to counter attacks. As the network traffic continues to grow, it can be challenging for network administrators to detect intrusions. In huge networks connected with millions of computers Terabytes/Zettabytes of data is generated every second. Deep Learning is an effective means for analyzing network traffic and detecting intrusions. In this article, distributed autoencoder on the CSE-CIC-IDS2018 dataset is implemented by considering all the classes of the dataset. The proposed work is implemented on Azure Cloud using distributed training as it helps in speeding up the training process, thereby detecting intrusions faster. An overall accuracy of 98.96 % is achieved. By leveraging such parallel computing into the security process, organizations may accomplish operations more quickly and respond to risks and remediate them at a rate that would not be possible with manual human capabilities alone.
In distributedsystems, situations often arise where some nodes each holds a collection of tokens, and all nodes collectively need to determine whether all tokens are distinct. For example, if each token represents a ...
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Traffic congestion greatly reduces efficiency and mobility in urban settings. Due to their reliance on set signal periods, traditional traffic signalization systems are unable to adequately handle the dynamic nature o...
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ISBN:
(纸本)9798350348798;9798350348804
Traffic congestion greatly reduces efficiency and mobility in urban settings. Due to their reliance on set signal periods, traditional traffic signalization systems are unable to adequately handle the dynamic nature of road traffic. This study presents a novel adaptive traffic signaling system that makes use of the Quality of Service (QoS) methodology, which is commonly used in computer network traffic management. Using a "traffic of interest" number to priorities roads, this technology dynamically modifies the duration of green lights to reflect current traffic circumstances. In order to ensure timely and effective traffic management, important factors such queue length, vehicle count, travel speed, and the presence of Emergency Vehicles (EmVs) are integrated into the system. EmV prioritization is a unique feature of the system that improves its functionality. According to preliminary findings (or simulations), there is a great deal of promise for easing traffic congestion and enhancing flow. The results of this study could completely change urban traffic management by providing a scalable and effective answer to a common urban problem.
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