Network Devices Research Articles

ANALISIS TRANSFER DATA PADA JARINGAN TERDAMPAK ARP SPOOFING MENGGUNAKAN METODE ARP POISONING DAN STATISTIK DESKRIPTIF

This Computer network security issues are very important and need to be considered in the development of computer networks. Networks connected to network devices are usually vulnerable to hacking. Hacking is an activity that allows a person or group to change or take data for personal gain. The aim of this research is to carry out testing and analysis to determine the condition and measure the level of security of the ITDA Yogyakarta intra-campus information system and computer network. Describe security gaps and measure the level of security that needs to be immediately repaired so that it can help correct failures in maintaining the security of ITDA Yogayakarta intra-campus information systems and networks. This research uses descriptive statistics with 20 PC units as samples. There were four tests in this study with a total success of 16 out of 20 samples. From the results of Arp spoofing on the local network, it can be concluded that after the local network is infiltrated by an attacker using the ARP spoofing method, the target traffic will be redirected to the attacker's device. This can allow attackers to monitor and understand the contents of data traffic on the local network. Changing the attacker's MAC address is very necessary because if the MAC is not replaced then network traffic will not be redirected to the attacker's device.

Cloud Computing Technology In Public Library System: An Overview

The present paper describes concept of cloud computing . Its Modal Advantage Of Cloud computing information & communication technology impact positively on library service & day by day change its service scenario know information It should be repacked and knowledge becomes information when it is externalized i.e. put into the process of communication . The paper has suggested / listed the importance & characteristics of the ICT in (LIS) library information service 7 component of cloud computing benefits & drawback of the technology . Cloud computing is an excellent alternative for educational institutions which are especially under budget shortage in order to operate their information systems effectively without spending any more capital for the computers & network device . Cloud computing brings the revolutionary changes in the words of information & communication technology because of its potential benefits . Such as reduced cost , accessible any where any time , as well its elasticity & flexibility

MONITORING ADMINSTRASI JARINGAN DENGAN MIKROTIK DAN TELEGRAM BOT PADA INTERNET SERVICE PROVIDER

Network management using MikroTik devices at Lajur Net still has shortcomings. Although internet access is available around the clock, the limited number of employees makes it difficult for managers to access computers to manage MikroTik. Administrators hope to manage the MikroTik network remotely through end devices. Currently, accessing MikroTik online incurs high operational costs for renting public IPs and web-based management application services. This study aims to utilize Telegram Bot for network management and monitoring that can be accessed remotely via the internet. The use of Telegram Bot for network management at Lajur Net can be successfully implemented. The implementation of Telegram Bot includes managing interfaces, hotspots, ICMP, and monitoring the resources of MikroTik RouterBoard devices in the Lajur Net network. Telegram Bot can be accessed remotely through the internet and works well on the Lajur Net network. The implementation of Telegram Bot includes interface administration, hotspots, ping, and monitoring the resources of MikroTik RouterBoard devices. The additional firewall feature allows network administrators to add and delete filter rules within the Lajur Net network security system. Moreover, Telegram Bot utilizes button features on Telegram messages to facilitate usage without having to type all commands. Thus, Telegram Bot offers an efficient and effective solution in MikroTik network management at Lajur Net, reducing the need for expensive public IPs and web-based management applications. This demonstrates that the use of chatbot technology can provide significant benefits in network administration, especially for internet service providers with limited resources. Through this research, it is hoped that the implementation of Telegram Bot can serve as a reference for developing more innovative and efficient network management in the future.

Multimode interference coupler based on general interference for integrated optical and quantum optic devices: a review

Integrated optics play important role in the development for optical network devices, optical processing, instrumentation and quantum information device. Here, multimode interference (MMI) coupler is one of basic component preferred for these integrated optic devices in which MMI coupler based on general interference (GIMMI) has been focused for large scale integrated optic devices because of compact size in comparison to MMI coupler based restricted interference (RIMMI). In this paper, we have reviewed different GIMMI structures reported previously. Different waveguide materials are mentioned and compared for fabrication of GIMMI device. The coupling behaviors and modal analysis of GIMMI couplers and their different tapered structures are estimated by using simple model based on sinusoidal modes showing reduced coupling lengths in down tapered structures in comparison to other structures. The excess loss, polarization dependence loss and crosstalk versus fabrication tolerances are obtained to compare their performances revealing almost same losses and fabrication tolerance. The paper reports the use of the GIMMI coupler in wavelength multiplexing, power splitting, switching, optical processing. Finally, Quantum optic application of GIMMI coupler is shown indicating high quantum fidelity of 50:50 coupling ratio.

Machine learning-based anomaly detection for smart home networks under adversarial attack

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

Machine learning-based anomaly detection for smart home networks under adversarial attack

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

Tangent sine search optimization enabled energy prediction and SWIPT‐based power transfer for energy‐aware communication in WSN

SummaryWith the rapid increase in energy utilization and the tremendously growing number of connected devices in wireless sensor networks (WSNs), alternate power transfer methods have not only become significant for scholastic purposes but also the growth of WSNs and the reduction of operational costs. Simultaneous wireless information and power transfer (SWIPT) is an emerging technology that has the potential to boost the lifespan of WSNs. This work presents an innovative approach to improving the energy efficiency of WSNs. Here, a WSN is considered, and energy‐aware communication is established in six phases, such as setup, steady state, energy prediction, SWIPT‐based power transfer, communication/route discovery, and route maintenance. Further, a hybrid approach named tangent sine search optimization (TSSO) is created to identify the ideal node as cluster head (CH). Later, the age of the neighboring nodes is forecasted utilizing the deep long short‐term memory (DLSTM), whose trainable parameters are selected using the proposed TSSO algorithm. A SWIPT‐based power transfer is also performed for harvesting the energy to avoid node failures. The proposed TSSO‐DLSTM+SWIPT is investigated given various metrics, like delay, residual energy, throughput, and trust, and the values attained are 0.784 s, 0.790 J, 0.970 Mbps, and 0.997, respectively without any attacks. The proposed TSSO‐DLSTM+SWIPT obtained performance improvement of 11.08%, 10.07%, 7.99%, and 5.83% than the Iterative algorithm in terms of delay, residual energy, throughput, and trust.

Stable Operation Domain Analysis of Back-to-Back Converter Based Flexible Multi-State Switches Considering PLL Dynamics

Flexible multi-state switches (FMSSs), also called soft open point (SOP), are regarded as the core devices in flexible interconnected distribution network. However, due to their more complex control structure and nonlinear characteristics, there is a tight and complex coupling relationship between the transmission power limit and stability of two (or more) converter sides, which brings difficulties to the planning and power flow dispatching control of flexible interconnected distribution networks. In this paper, taking the back-to-back converter based FMSS (BTBC-FMSS) as the research object, the dynamic equivalent circuit connected to the distribution network is constructed, and the transmission active and reactive power expression of the converter on both sides considering PLL dynamics is deduced. The variation rules of transmission power limits on both sides of the device with various structures and control parameters are summarized. Furthermore, the stable operation domain of the BTBC-FMSS is constructed, and the effective measures to improve the transmission power stability of the device are given according to the analysis of the stable operation domain. Finally, a simulation model is built in Matlab/Simulink to verify the effectiveness of the analysis results.

Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.

Improvement of the secure routing model in software-configured networks

Currently, the deployment of such network architectures as Software-Defined Networking (SDN) is facing new cyber security threats that require the development and research of new specialized solutions to increase the level of network security. Despite its high openness and programmability, the SDN architecture replaces the traditional network, but it increases the number of potential network attacks, which leads to new security problems. The growing interest in SDN and the widespread deployment of software-configured networks of various types allow identifying their shortcomings in the process of combating cyber security threats. Obviously, security issues are closely related to the characteristics of SDN networks themselves. Furthermore, security issues in SDN can be divided based on three layers: data plane, control plane, and application plane. At the same time, devices of different SDN levels can be among the objects of attacks. Therefore, according to the multilayer architecture of SDN, security threats can be classified at the data transmission, management and application layers. For its part, the data plane consists of switches and other network devices and is mainly responsible for data processing, forwarding, discarding, and collecting statistics. The data plane functions on the basis of flow rules provided by the network controller. While the main causes of security problems are the SDN architecture itself, external malicious attacks, insufficient access control and encryption tools. Today, an important place in the complex of means of increasing network security, including SDN networks, is given to routing protocols, which require the systematic and coordinated interaction of a number of network elements at the same time – SDN switches and network controllers during the formation (calculation) of paths and flow rules, along which the required level of security must be ensured according to selected indicators or criteria. The paper analyzes how to modify route metrics in such a way that the resulting model acquires the properties of secure QoS routing. It is shown that the improvement of the model and the choice of the route should be chosen taking into account the basic metrics of the criticality of vulnerabilities and the bandwidth of the communication channels that make up this route.

Enhancing multi-plane light conversion orbital angular momentum multiplexer performance via error analysis.

We present what we believe to be a novel orbital angular momentum (OAM) multiplexing apparatus capable of handling topological charges ranging from l = 0 to ±3 through multi-plane light conversion (MPLC) across four phase planes. Nevertheless, the fabricating process of MPLC devices is prone to errors that cannot be avoided. Our investigation primarily delves into the repercussions of misalignment and etching inaccuracies on the device's phase, with the assistance of a spatial light modulator. The scrutiny of fabrication errors associated with the device offers valuable insights for refining the fabricating of MPLC devices. The OAM multiplexing device converts the phase of MPLC onto a glass substrate through four etching steps, corresponding to a depth of 0-775 nm. OAM multiplexing/demultiplexing crosstalk based on MPLC is less than -20 dB and -18 dB, respectively. The insertion loss of the OAM mode generated by the OAM multiplexing device coupled to the few-mode fiber is less than 7 dB. In a communication experiment, we demonstrated multiplexed three OAM channels carrying 10 Gbit/s OOK signals over a 5 km few-mode fiber using two MPLC devices. Both the bit error rate curve and constellation diagram demonstrate the excellent performance of MPLC-based OAM multiplexing devices in communication networks.

Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

Multi-Access Edge Computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (nmpc) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework’s effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Navigating latency hurdles: an in-depth examination of a cloud-powered GNSS real-time positioning application on mobile devices

A growing dependence on real-time positioning apps for navigation, safety, and location-based services necessitates a deep understanding of latency challenges within cloud-based Global Navigation Satellite System (GNSS) solutions. This study analyses a GNSS real-time positioning app on smartphones that utilizes cloud computing for positioning data delivery. The study investigates and quantifies diverse latency contributors throughout the system architecture, including GNSS signal acquisition, data transmission, cloud processing, and result dissemination. Controlled experiments and real-world scenarios are employed to assess the influence of network conditions, device capabilities, and cloud server load on overall positioning latency. Findings highlight system bottlenecks and their relative contributions to latency. Additionally, practical recommendations are presented for developers and cloud service providers to mitigate these challenges and guarantee an optimal user experience for real-time positioning applications. This study not only elucidates the complex interplay of factors affecting GNSS app latency, but also paves the way for future advancements in cloud-based positioning solutions, ensuring the accuracy and timeliness critical for safety–critical and emerging applications.

Optimizing OSPF Cost Value for Best Path Selection Using Metric-Type Variation

Open Shortest Path First (OSPF) is a multi-vendor routing standard. Any routing protocol's goal is to discover the optimum path between source and destination addresses inside a computer network. Other routing protocols include Routing Information Protocol (RIP), Border Gateway Protocol (BGP), Enhanced Interior Gateway Routing Protocol (EIGRP), and IS-IS. which are also used in computer networking. Each routing protocol has different set of rules through which they calculate the best path selection. OSPF is often used with the combination of these routing protocol because of its flexible design. This research focuses on the implementation of OSPF network cost (metric) value for best path calculation using network simulation tool like GNS3 for getting correct outcome just like a real networking device working in a live production environment. This research would use the different combinations of OSPF cost calculation using metric-types for external routes learned from different routing protocols like RIP. Being able to accurately identify cost (metric) values obtain by testing for OSPF when used with other routing protocols, just like a real-world environment.

Troubleshooting solution for traffic congestion control

The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently, Google has developed BBR (Bottleneck Bandwidth and Round-Trip Time), a rate-based congestion control algorithm. BBR controls transmission rates based on delivery rate and round-trip time (RTT). However, such a static congestion control algorithm (e.g., BBR, etc.) cannot achieve high performance in various network conditions (e.g., low bandwidth, etc.). Concretely, these static algorithms cannot adapt to the dynamic changes of the network environment. Therefore, in this paper, we propose an adaptive algorithm (called ABBR) for congestion control in next-generation networks. ABBR takes into account the reinforcement learning algorithm to learn relevant policies to change the transmission rate corresponding to each congestion control algorithm to optimize long-term performance. The experimental results show that our proposal can achieve good performance in terms of throughput, RTT, and fairness compared to the benchmarks.

Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms

Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of intrusion detection. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study developed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifiers. The model was also tested without a mayfly algorithm. The model's efficacy was determined through a comparison of its accuracy, specificity, precision, sensitivity, and F1 score. The experimental outcomes revealed that the proposed model is more efficient than existing models based on the performance evaluation and the CIC-IDS 2017 dataset employed in this research. Accuracy scores of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. In comparison to existing models, the proposed model yielded better results in terms of accuracy, sensitivity, specificity, and F1-score metrics. The model's sturdiness can be attributed to the use of mayfly techniques, which harness the strength in PSO, GA and FA for selecting optimal feature subsets. The results of this research provide a reliable dimensionality reduction model that may be used in the field of computer networks for intrusion detection and enhancement of security in computer networking environments.

Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a Conditional Variational Autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly-available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI-based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1-score loss compared to training on real data; competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.