Switching Networks Research Articles

Hostile-based bipartite containment control of nonlinear fractional multi-agent systems with input delays: a signed graph approach under disturbance and switching networks

<abstract><p>This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided.</p></abstract>

Exploring a novel framework for DoS/DDoS attack detection and simulation in contemporary networks

Currently, the internet serves as the predominant means of communication and is utilized by a vast number of individuals worldwide. Simultaneously, the commercial aspect of the internet is contributing to a rise in susceptibility to cybercrimes, leading to a significant surge in the occurrence of distributed Denial of Service (DDoS) assaults over the last decade. DoS/DDoS assaults primarily target network resources such as network bandwidth, CPU time, memory consumption, web servers, and network switches. Network security is an essential and crucial problem in the modern interconnected society. Numerous studies have been undertaken by multiple researchers thus far in order to identify this attack. However, there is still room for improvement in past investigations. This paper presents a novel approach for detecting and simulating DoS/DDoS attacks in modern networking environments, introducing a new paradigm. It is done in a controlled environment. The primary focus of this work is to simulate an attacker's perspective of a DoS/DDoS attack by repeatedly sending huge SYN flood packets to a specific target or network server using the hping3 tool. On the server side, the proposed attacker detector script continuously monitors incoming network connections on the network server using the netstat command. It identifies potential DoS/DDoS attacks by analyzing the connection count and comparing connections count with an assumed threshold. This experiment results in 61% CPU usage and 7.1% memory consumption while a DDoS attack triggers on the target server. Additionally, the proposed script performs statistical analysis and displays warning messages on the console when suspicious activity is detected on the network server. Wireshark is also utilized in this work to detect anomalous network traffic patterns in order to identify distributed denial-ofservice (DDoS) attacks that are targeting a network server. Additionally, it offers the capability to block the IP address of the attacker if the configuration allows for it. This proposed approach efficiently identifies DDoS activity in real-time network traffic, further helping to improve network security.

Green ICT Methodology for Energy Consumption Calculation in ICT Architecture Components

As the usage of information and communication technology (ICT) solution have been exploded in the last decades, the research community exhibits a deficiency in terms of methodologies for accurately quantifying the energy consumption of individual ICT components. This paper presents a novel methodology aimed at addressing the existing research gap by offering a comprehensive analysis of energy consumption ranges for ICT components, measured in Watt. The methodology being proposed involves the classification of ICT components into distinct classes, namely sensors, gateways, servers-platforms, and databases, while these classes are further subdivided into sub-classes. The calculation of energy consumption places emphasis on particular components within each sub-class, encompassing CPUs, RAM, GPUs, hard drives, network switches, cooling systems, and communication units. Compared with existing green ICT standards where mainly focused on strategy-related recommendations, the methodology utilises manufacturers’ specifications for standard operational procedures in order to compute energy consumption. The objective of the methodology is to extend the current research and provide countable energy consumption attributes of various component types, that are used in the ICT architecture, supporting the decision-making process of architects regarding environmentally sustainable and high-performance device selection. This study concludes with the development of a tree diagram for calculating the energy consumption of an ICT system.

Retracted: A Novel Distributed Denial of Service (DDoS) Detection Method in Software Defined Networks

The software Defined Networks (SDN) are the most promising solutions in solving challenges regarding complex network control. The SDNs are responsible for separating network control functions from the network switch. By applying SDNs, communication companies and operators can control and manage networks through one central controller unit therein with no need by applying hardware or the manufacture's guideline. Lack of complex DDoS attack detection, lack of rapid detection sensitivity on packet type attack, lack of methods in promoting detection attack to specific unknown protocols, lack of ability in detecting at switch level, lack of assessing attacks packet types, constitute drawbacks. Although is made in this article to overcome these drawbacks, consequently the GainRatioAttributeEval algorithm and Ranker method are applied. The datasets involved here are the ISCX-SlowDDoS2016, ISCX-IDS2012, CTU-10 and CTU-11.This proposed method is compared with similar methods and it is found that this method with 99.95% accuracy and 0.04 FPR for UNB-ISCX dataset and 99.84% accuracy and 0.1 FPR for CTU-13 dataset is better than others.

An Enhanced Dynamic Multi-Path Routing Algorithm for Software Defined Networks

Software-defined networking (SDN) is an emerging technology that addresses the limitations of legacy networks when handling the growing traffic demands. Legacy networks face issues like heavy traffic loads, congestion and delays. SDN offers a solution to these challenges. However, an efficient routing algorithm is required to transmit data packets through the network. While there are existing routing algorithms, there remains a need for a highly efficient one that minimizes packet loss and optimizes link utilization. One commonly used routing for mitigating issues, such as packet loss, delay and bandwidth allocation, is multipath routing. The inherent flexibility of SDN, which provides a comprehensive overview of network status, empowers routing algorithms to generate efficient routing paths. We introduce the enhanced dynamic multipath routing (EDMR) algorithm in this context. This algorithm dynamically generates and updates routing paths within the network's switches. Its dynamic nature significantly reduces packet loss and minimizes end-to-end delays.

Proportional-Integral Observer-Based State Estimation for Markov Memristive Neural Networks With Sensor Saturations.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

In Situ Study of Self-Organized Memristive Switching in Neuromorphic Nanoparticle Networks using Complementary SEM Methods

In Situ Study of Self-Organized Memristive Switching in Neuromorphic Nanoparticle Networks using Complementary SEM Methods

A repetitive frequency square wave generator based on Blumlein pulse forming network and pseudospark switch.

The Blumlein pulse forming network (PFN) has widely beenused in pulsed power technology to generate square waves with short pulse widths. In this paper, we developed a repetitive frequency square wave generator based on Blumlein PFN and pseudospark switch (PSS). A Blumlein PFN with unequal capacitances has been proposed, and the PFN parameters have been optimized for better output waveforms. A single-gap PSS with a withstand voltage of 40kV and a repetitive frequency of 100Hz has been designed to switch the Blumlein PFN. The experiment results show that the square wave generator can output pulses with a voltage of 26kV, a rise time of 25ns, and a pulse width of 90ns on a matched load of 11Ω. It has operated steadily for 10h with a repetitive frequency of 100Hz, and the jitter remains at around 1ns after 1.05 × 106 shots.

An N/PBTI-Isolated BTI Monitor With a Configurable Switching Network and Calibration for Process Variation in Memory Periphery

An N/PBTI-Isolated BTI Monitor With a Configurable Switching Network and Calibration for Process Variation in Memory Periphery

Adaptive Funnel Synchronization Control for Complex Switching Networks in Power Systems

Adaptive Funnel Synchronization Control for Complex Switching Networks in Power Systems

Dynamic mobile X-haul reconfiguration by fast network switching for low-latency MEC service

Dynamic mobile X-haul reconfiguration by fast network switching for low-latency MEC service

State Estimation of Networked Switched Systems via Event-Triggered Zonotopes

State Estimation of Networked Switched Systems via Event-Triggered Zonotopes

Hardware Design and Evaluation of an FPGA-Based Network Switch Supporting Asynchronous Traffic Shaping for Time Sensitive Networking

Hardware Design and Evaluation of an FPGA-Based Network Switch Supporting Asynchronous Traffic Shaping for Time Sensitive Networking

A Cross-Layer Game-Theoretic Approach to Resilient Control of Networked Switched Systems Against DoS Attacks.

This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player's side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an H∞ minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

Genetic-Algorithm-Based Sliding Mode Stabilization for Networked Switched Systems With Unreliable Channels

Genetic-Algorithm-Based Sliding Mode Stabilization for Networked Switched Systems With Unreliable Channels

INO80-Dependent Remodeling of Transcriptional Regulatory Network Underlies the Progression of Heart Failure.

Progressive remodeling of cardiac gene expression underlies decline in cardiac function, eventually leading to heart failure. However, the major determinants of transcriptional network switching from normal to failed hearts remain to be determined. In this study, we integrated human samples, genetic mouse models, and genomic approaches, including bulk RNA sequencing, single-cell RNA sequencing, chromatin immunoprecipitation followed by high-throughput sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing, to identify the role of chromatin remodeling complex INO80 in heart homeostasis and dysfunction. The INO80 chromatin remodeling complex was abundantly expressed in mature cardiomyocytes, and its expression further increased in mouse and human heart failure. Cardiomyocyte-specific overexpression of Ino80, its core catalytic subunit, induced heart failure within 4 days. Combining RNA sequencing, chromatin immunoprecipitation followed by high-throughput sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing, we revealed INO80 overexpression-dependent reshaping of the nucleosomal landscape that remodeled a core set of transcription factors, most notably the MEF2 (Myocyte Enhancer Factor 2) family, whose target genes were closely associated with cardiac function. Conditional cardiomyocyte-specific deletion of Ino80 in an established mouse model of heart failure demonstrated remarkable preservation of cardiac function. In summary, our findings shed light on the INO80-dependent remodeling of the chromatin landscape and transcriptional networks as a major mechanism underlying cardiac dysfunction in heart failure, and suggest INO80 as a potential preventative or interventional target.

Environmental impacts of a digital health and well-being service in elderly living schemes

Over the past decade, digitalization and digital technologies (DTs) have undergone rapid evolution, transforming how goods and services are produced and consumed in modern societies. Health and well-being sectors have embraced this digital revolution. Besides the economic and social benefits, digitalization can significantly enhance patient diagnostics and prognostics while improving overall service efficiency. To ensure long-term sustainability, it is important to assess and reduce the environmental impacts of digital services. This article examines the life cycle impacts of a digital service implemented in three elderly living schemes (ELSs) located in the United Kingdom (UK). The digital service consists of six electronic devices (EDs) that enable communication between residents, visitors, staff, and offsite monitoring (OM). The equipment is connected using Power over Ethernet (PoE) technology, which includes smart network switch and uninterruptable power supply units. The digital service's global warming potential (GWP) was estimated at 718–741 kg CO2 eq./resident for two of the ELSs and 1509 kg CO2 eq./resident for a third ELS, considering a service period of 20 years. The reason for the significant difference is the greater use of air conditioner (A/C) units to cool down server rooms and fewer residents in the third scheme. The consumption of electricity was found to be the main contributor to most of the environmental impacts. However, in certain categories such as mineral resource scarcity, freshwater eutrophication, and freshwater and marine ecotoxicity potentials, printed circuit boards (PCBs) were the main contributors. A sensitivity analysis considering different national electricity mixes demonstrated that the French electricity grid promoted the reduction in 14 impact categories, whereas the German, Italian, Spanish and Japanese grids increased on average impacts on most categories. Another sensitivity analysis demonstrates that reducing A/C unit running time by 28% resulted in an average impact reduction of 5.5%, becoming equivalent to the results obtained for the French electricity grid. Finally, extending the expected lifespan of electronic equipment by 20% yielded the highest average decrease in environmental impacts (8.1%). While digitalization has the potential to enhance patient healthcare and reduce costs, it is crucial to carefully assess its environmental impacts and implement mitigation strategies to ensure sustainable development in the healthcare sector.

Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds

Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To address this challenging task, a novel modular hierarchical reinforcement learning (MHRL) method is developed in this paper. MHRL is composed of three modules, i.e., destination evaluation, policy switch, and motion network, which are designed exactly according to the three phases of solving the original navigation problem. First, the destination evaluation module rates all destinations and selects the one with the lowest cost. Subsequently, the policy switch module decides which motion network to be used according to the selected destination and the obstacle state. Finally, the selected motion network outputs the robot action. Owing to the complementary strengths of a variety of motion networks and the cooperation of modules in each layer, MHRL is able to deal with hybrid crowds effectively. Extensive simulation experiments demonstrate that MHRL achieves better performance than state-of-the-art methods.

Detection and Mitigation of DDOS Attack in SDN Environment Using Hybrid CNN-LSTM

DDoS attacks, powered by botnets to flood network resources, pose a significant threat to traditional network setups. Software-Defined Networking (SDN) boosts network adaptability and programmability by separating the control and data planes. However, the centralized control in SDN can be a vulnerability, allowing attackers to exploit security flaws and launch DDoS attacks. These attacks overwhelm network controllers and switches, consuming bandwidth and server resources, and disrupting regular user access. In response to the threat, we've implemented an online SDN defence system designed to detect and counter such attacks. This system includes modules for both spotting anomalies and handling them. The anomaly detection model combines Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) techniques to effectively spot irregular traffic patterns. For mitigation, the model identifies abnormal traffic by implementing flow rule orders from the controller and traces back to the attacker via IP tracing. To measure our approach's effectiveness, we used various evaluation metrics like Accuracy, F-measure, Precision, Recall, ROC Curve, and Precision-Recall Curve. Our methodology displayed impressive results, achieving a 99.83% accuracy in multiclass classification and 99.17% accuracy in binary classification. In comparison with existing DDoS detection systems, our AI-driven mitigation techniques demonstrated their superiority. Overall, our research aims to streamline the detection and mitigation of DDoS attacks.

Detection of Burst Header Packet Flooding Attacks via Optimization based Deep Learning Framework in Optical Burst Switching Network

Detection of Burst Header Packet Flooding Attacks via Optimization based Deep Learning Framework in Optical Burst Switching Network