Internet Service Provider Networks Research Articles

PERANCANGAN E - TRANSAKSI PEMBAYARAN INTERNET SERVICE PROVIDER PADA PT DIGITAL NETWORK ANTANUSA (DNA.NET) KOTA JAMBI

PT Digital Network Antanusa (DNA.Net) Jambi City is an internet service provider network service company in the city of Jambi. DNA.Net operates using cable and wireless network technology (wireless). DNA.Net is not a cellular or satellite service, but something new, much better and much faster to connect to the internet and provide to homes or companies. However, there are still problems with processing, paying and checking internet bills, therefore errors and files are often lost when processing payment transaction data. These include frequent errors in internet registration data information and lost documents, delays in transactions or transactions taking too long, and even making payment reports is still not neat. This method used is the UML (Unified Modeling Language) method which describes the system model including use cases, use case specifications, activities and class diagrams and the designs used by researchers are prototypes. Therefore, the researcher aims to analyze and design an internet service provider payment transaction information system so that a computerized payment system is more flexible and efficient so that problems at PT Digital Network Antanusa can be resolved so that the obstacles currently faced can be resolved and run very well. . The final result of the analysis and design produces a maturity system. This is done to make the system more effective and efficient and of course to make the bill payment process easier

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture.

As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching-Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture. The increasing adoption of technology in agriculture relies heavily on real-time data, remote monitoring, and automated processes. This dynamic nature requires robust and reliable high-bandwidth connections to facilitate data flow between sensors, devices, and central management systems. By optimizing bandwidth utilization through MPLS-TE and implementing traffic control mechanisms with Diffserv-QoS, ISPs can create a resilient network foundation for smart agriculture applications. The integration of MPLS-TE and Diffserv-QoS has resulted in significant enhancements in throughput and a considerable reduction in Jitter. Employment of the IPv4 header has demonstrated impressive outcomes, achieving a throughput of 5.83 Mbps and reducing Jitter to 3 msec.

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management.

The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons-six, nine, and twelve steps-demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model's performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.

Routing Optimization With Deep Reinforcement Learning in Knowledge Defined Networking

Traditional routing algorithms cannot dynamically change network environments due to the limited information for routing decisions. Meanwhile, they are prone to performance bottlenecks in the face of increasingly complex business requirements. Some approaches, such as deep reinforcement learning (DRL) have been proposed to address the routing problems. However, they hardly utilize the information about the network environment fully. The Knowledge Defined Networking (KDN) architecture inspires us to develop new learning mechanisms adapted to the dynamic characteristics of the network topology. In this paper, we propose an effective scheme to solve the routing optimization problem by adding a graph neural network (GNN) structure to DRL, called Message Passing Deep Reinforcement Learning (MPDRL). MPDRL uses the characteristics of GNN to interact with the network topology environment and extracts exploitable knowledge through the message passing process of information between links in the topology. The goal is to achieve the load balance of network traffic and improve network performance. We have conducted experiments on three Internet Service Provider (ISP) network topologies. The evaluation results show that MPDRL obtains better network performance than the baseline algorithms.

Dynamic Inference From IoT Traffic Flows Under Concept Drifts in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to mitigate risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying datadriven inference models when labeled data of device behaviors is limited and the distribution of data changes across time and space domains (concept drifts). Our contributions are fourfold: (1) We collect and analyze more than six million network traffic flows of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behaviors of IoT devices -we publicly release our training and testing instances data; (2) We analyze the performance of two inference strategies, namely “global inference” (a model trained on a combined set of all labeled data from training homes) and “contextualized inference” (several models each trained on the labeled data from a training home) in the presence of concept drifts; (3) To manage concept drifts, we develop a method that dynamically applies the “best” model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in a fifth of scenarios when the labels are available for the testing data (ideal but unrealistic settings); and (4) We develop a method to automatically select the best model without needing labels of unseen data (a realistic inference) and show that it can achieve 94% of the ideal model’s accuracy.

Optimizing incremental SDN upgrades for load balancing in ISP networks

Software defined network (SDN) can dynamically and timely reply to the changes of network states, thus enabling advance traffic engineering mechanisms. To enhance the management ability of the network, Internet Service Providers (ISPs) are upgrading traditional network devices to SDN devices incrementally. In this paper, we study the k-LB problem, i.e., upgrading at most k legacy switches to SDN switches to achieve load balance. We prove that k-LB problem is NP-hard and there is no polynomial time (N+M)1−ϵ-approximation algorithm for any constant ϵ>0 unless P=NP, where N (M) is the number of switches (links) in the network. Nevertheless, we propose an effective greedy algorithm and prove that it reaches an approximation guarantee of cavgcminM, where cavg (cmin) is the average (minimum) link capacity. Furthermore, we show that the greedy algorithm touches the tight lower bound of approximation ratio by extending the inapproximability result. The simulation results from large-scale ISP network topologies illustrate the effectiveness of our algorithm and show that the maximum link utilization can be decreased by 30% on average compared with the SOTA.

Combining Stochastic and Deterministic Modeling of IPFIX Records to Infer Connected IoT Devices in Residential ISP Networks

Residential Internet service providers (ISPs) today have limited device-level visibility into subscriber houses, primarily due to the network address translation (NAT) technology. The continuous growth of “unmanaged” consumer Internet of Things (IoT) devices combined with the rise of work-from-home makes home networks attractive targets to sophisticated cyber attackers. Volumetric attacks sourced from a distributed set of vulnerable IoT devices can impact ISPs by deteriorating the performance of their network, or even making them liable for being a carrier of malicious traffic. This article explains how ISPs can employ IP Flow Information eXport (IPFIX), a flow-level telemetry protocol available on their network, to infer connected IoT devices and ensure their cyber health without making changes to home networks. Our contributions are threefold: 1) we analyze more than nine million IPFIX records of 26 IoT devices collected from a residential testbed over three months and identify 28 flow features pertinent to their network activity that characterize the network behavior of IoT devices—we release our IPFIX records as open data to the public; 2) we train a multiclass classifier on stochastic attributes of IPFIX flows to infer the presence of certain IoT device types in a home network with an average accuracy of 96%. On top of the machine learning (ML) model, we develop a trust metric to track network activity of detected devices over time; and 3) finally, we develop deterministic models (DTs) of specific and shared cloud services consumed by IoTs, yielding an average accuracy of 92%. We show a combination of stochastic and DTs mitigates false positives in 75% of incidents at the expense of an average 7% reduction in true positives.

Analysis of network function sharing in Content Delivery Network‐as‐a‐service slicing scenarios

AbstractVideo content consumption is currently dominating the mix of traffic observed in Internet service provider (ISP) networks. The distribution of that content is usually performed by means of content delivery network (CDN) caches storing and delivering multimedia. The advent of virtualization is calling attention to the CDN providers as a use case for virtualizing the cache function. In parallel, there is a trend for sharing network infrastructures as a way of reducing deployment costs by ISPs. Then, an interesting scenario emerges when considering the possibility of sharing virtualized cache functions among ISPs which are sharing a common physical infrastructure, mostly considering that those ISPs offer similar video content catalogs to end users. Furthermore, when deciding to share the caches among ISPs, it is interesting to understand the impacts due to limiting the storage capacity at the edge sites. This paper investigates through simulations the potential efficiencies that can be achieved when sharing a virtual cache function if compared with the classical approach of independent virtual caches operated per ISP, as well as the implications of limiting the storage capacity of the caches at the edge.

Traffic classification in an increasingly encrypted web

Traffic classification is essential in network management for a wide range of operations. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, for example, as a de facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using deep learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations are commonly ignored. In this paper, we design a novel feature engineering approach used for encrypted Web protocols, and develop a neural network architecture based on stacked long short-term memory layers and convolutional neural networks. We evaluate our approach on a real-world Web traffic dataset from a major Internet service provider and mobile network operator. We achieve an accuracy of 95% in service classification with less raw traffic and a smaller number of parameters, outperforming a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted Web protocols. We also evaluate our approach on a public QUIC dataset with finer application-level granularity in labeling, achieving an overall accuracy of 99%.

Proactive Metering of Mobile Internet User Experience

Having 67% worldwide share, mobile internet is very important for Internet Service Providers (ISPs). Since mobile Internet access is a collective service, Key Performance Indicators (KPIs) measuring quality of data traffic on select network segments/servers may not correctly indicate true user experience. For this reason, mobile ISPs are investing in sophisticated high-end commercial speed analysis systems which typically collect and analyze network traffic data from key network segments/servers. Unfortunately, their utility is quite limited as long as the proactive network intervention is considered. In this work, we develop a MapReduce based network speed analysis system which measures end-to-end network speed to quantify true user experience across multiple geographic regions and service categories. Also functioning as an online decision support system, it enables network administrators with timely ISP network intervention right before potential arrival of mass number of user complaints. The system has been tested with a leading mobile ISP in Turkey. The results confirm its effectiveness.

Routing Performance Evaluation of a Multi-Domain Hybrid SDN for Its Implementation in Carrier Grade ISP Networks

Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need of minimizing the capital and operational expenditure of running network infrastructure. The implementation of software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy networks need to integrate with the SDN networks for smooth migration towards the fully functional SDN environment. In this paper, we compare the network performance of the legacy network with the SDN network for IP routing in order to determine the feasibility of the SDN deployment in the Internet Service provider (ISP) network. The simulation of the network is performed in the Mininet test-bed and the network traffic is generated using a distributed Internet traffic generator. An open network operating system is used as a controller for the SDN network, in which the SDN-IP application is used for IP routing. Round trip time, bandwidth, and packet transmission rate from both SDN and legacy networks are first collected and then the comparison is made. We found that SDN-IP performs better in terms of bandwidth and latency as compared to legacy routing. The experimental analysis of interoperability between SDN and legacy networks shows that SDN implementation in a production level carrier-grade ISP network is viable and progressive.

Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web

Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations are commonly ignored. In this paper, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN). We evaluate our approach on a real-world web traffic dataset from a major Internet service provider and Mobile Network Operator. We achieve an accuracy of 95% in service classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer application-level granularity in labeling, achieving an overall accuracy of 99%.

Deployable Models for Approximating Web QoE Metrics From Encrypted Traffic

Being on endpoints, Content Providers can easily evaluate end users' Web browsing quality of experience (Web QoE) by accessing in-browser computed application-level metrics. Because of end-to-end traffic encryption, it is becoming considerably harder for Internet Service Providers (ISPs) to evaluate the Web QoE of their customers, which is important for management purposes. In this paper, we propose data-driven machine learning techniques and exact flow-level algorithmic methods to infer well-known application-level Web performance metrics (such as SpeedIndex and Page Load Time) from raw encrypted streams of network traffic. We prove the efficiency of our approach taking as input a unique dataset of more than 200,000 experiments, targeting a large set of popular pages (Alexa top-500), from probes from several ISPs networks, with different browsers (Chrome, Firefox) and viewport combinations. Results show that our data-driven models are not only accurate for several Web performance metrics, but also feature the ability to generalize to previously unseen conditions. Furthermore, we discuss how our extremely lightweight flow-level method has a provable accuracy on a specific metric, and is thus of particular appeal from a deployment viewpoint.

A Game-Theoretic Approach for Enhancing Data Privacy in SDN-Based Smart Grids

Smart grids rely on communication networks to connect the physical devices and the control and computation technologies. The transmission of sensitive data over the network induces the possibility of leakage of private and sensitive information about various entities and components in the grid. To address this issue, this article proposes a privacy-preserving framework to enhance the privacy of smart grids integrated with software-defined networks. The proposed framework uses two privacy metrics (mutual information and differential privacy) and formulates a privacy-preserving distributed optimization algorithm with the objective of minimizing the network cost. We view the distributed optimization algorithm as an n-player, noncooperative game and provide distributed techniques to solve the optimization problem efficiently. We prove that our algorithm converges to the Nash equilibrium of the game while preserving the data privacy. We validate the performance of our approach using three IEEE bus systems and realistic Internet service provider network topology.

Failure Resiliency With Only a Few Tunnels – Enabling Segment Routing for Traffic Engineering

Traffic engineering is an important concept that allows Internet Service Providers (ISPs) to utilize their existing routing hardware more efficiently. One technology that can be used is Segment Routing (SR). In this paper, we address the use of SR to increase the resilience against failure scenarios. In addition, we develop solutions that are manageable and, thus, deployable in a tier 1 ISP network. We propose a post-convergence aware SR based optimization model. With it, we can proactively find a single SR configuration that is beneficial in all predefined failure scenarios, including single link failures, shared risk link group failures, and node failures. In addition to this use-case, we also extend the optimization model to include other important practical requirements such as keeping the number of SR tunnels to a minimum, avoiding arbitrary traffic splitting, or meeting latency bounds. We evaluate our approaches with recently measured data from a tier 1 ISP and show that we can improve over state of the art routing approaches.

Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

Maintaining a satisfactory customer Quality of Experience (QoE) is of vital importance for video service providers such as Netflix or Amazon Prime Video. Network faults degrade QoE and must therefore be detected, isolated, and fixed. However, this is difficult because each part of the end-to-end path belongs to a different autonomous system (AS) that is typically owned by a different entity, such as the video streaming provider, the internet service provider (ISP), and the client's local network operator. Although the video service provider (VSP) is usually blamed by the customer when there is poor QoE, the VSP does not have access to many parts of the network to localize the issue. In this paper, we show that with the aid of AI, it is possible for the VSP to localize the network fault without having access to the faulty part and using only QoE metrics. We collected a dataset from an actual video streaming testbed, where multiple videos are streamed from a video server through a simplified ISP network to a client network. Actual faults were generated in both the ISP and the client networks. Using only the QoE metrics measured at the client side, we use the deep learning methods of multi-layer perceptron (MLP) and long-short-term memory (LSTM) to detect and localize the fault with an accuracy of 93–97%, depending on the situation. Impact Statement —Technologically, our work impacts video/game streaming service providers such as Netflix, YouTube, Amazon Prime, Google Stadia, Sony PlayStation Now, Nvidia GeForce Now, and videoconferencing providers such as Zoom and Skype. Our work enables these providers to train similar AI systems that can localize network problems using only the video quality of experience (QoE) recorded by their client software. They can then take an appropriate action, such as rerouting traffic using Open Connect Appliances (OCA) if available, using another network provider if they have contracts with more than one, or informing the owner of the network segment with the fault, so they can fix the problem and maintain their customers’ QoE at a satisfactory level. Economically, our work can contribute to the market expansion of any video streaming solution because it will lead to better QoE, which is synonymous with more customers.

Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment

The logical separation of the data plane and the control plane of the network device conceptually defined by software-defined networking (SDN) creates many opportunities to create smart networking with better efficiency for network management and operation. SDN implementation over telecommunications (Telcos) and Internet service provider (ISP) networks is a challenging issue due to the lack of a high maturity level of SDN-based standards and several other critical factors that are considered during the real-time migration of existing legacy IPv4 networks. Different migration approaches have been studied; however, none of them seem to be close to realizing implementation. This paper implements the SDN-IP and Open Network Operating System (ONOS) SDN controller to migrate legacy IPv4 networks to multi-domain software-defined IPv6 (SoDIP6) networks and experimentally evaluate the viability of joint network migration in the ISP networks. We present results using extensive simulations for the suitable placement of the master ONOS controller during network migration by considering minimum control path latency using optimal path routing and the breadth first router replacement (BFR) technique. Our empirical analysis and evaluations show that the identification of the median router to attach the master controller and router migration planning using BFR give better results for carrier-grade legacy networks’ migration to SoDIP6 networks.

Evolutionary gaming approach for decision making of Tier‐3 Internet service provider networks migration to SoDIP6 networks

SummaryWith the increasing number of Internet of Things (IoT) devices, current networking world is suffering in terms of management and operations with lack of IPv4 addresses leading to issues like network address translation (NAT) proliferation, security and quality of services. Software‐defined networking (SDN) and Internet Protocol version 6 (IPv6) are the new networking paradigms evolved to address related issues of legacy IPv4 networking. To adapt with global competitive environment and avoid all existing issues in legacy networking system, network service providers have to migrate their networks into IPv6 and SDN‐enabled networks. But immediate transformations of existing network are not viable due to several factors like higher cost of migration, lack of technical human resources, lack of standards and protocols during transitions, and many more. In this paper, we present the migration analysis for proper decision making of network transition in terms of customer demand, traffic engineering, and organizational strength with operation expenditure for network migration using evolutionary gaming approach. Joint migration to SDN‐enabled IPv6 network from game theoretic perspective is modeled and is validated using numerical results obtained from simulations. Our empirical analysis shows the evolutionary process of network migration while different internal and external factors in the organization affect the overall migration. Evolutionary game in migration planning is supportive in decision making for service providers to develop suitable strategy for their network migration. The proposed approach for migration decision making is mostly applicable to fairly sustained service providers who lack economics, regulation/policy, and resources strengths.

A DDoS Attack Mitigation Scheme in ISP Networks Using Machine Learning Based on SDN

Keeping Internet users protected from cyberattacks and other threats is one of the most prominent security challenges for network operators nowadays. Among other critical threats, distributed denial-of-service (DDoS) becomes one of the most widespread attacks in the Internet, which is very challenging to mitigate appropriately as DDoS attacks cause the system to stop working by resource exhaustion. Software-defined networking (SDN) has recently emerged as a new networking technology offering unprecedented programmability that allows network operators to configure and manage their infrastructures dynamically. The flexible processing and centralized management of the SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a novel DDoS attack mitigation in SDN-based Internet Service Provider (ISP) networks for TCP-SYN and ICMP flood attacks utilizing machine learning approach, i.e., K-Nearest-Neighbor (KNN) and XGBoost. By deploying a testbed, we implement the proposed algorithms, evaluate their accuracy, and address the trade-off between the accuracy and mitigation efficiency. Through extensive experiments, the results show that the algorithms can efficiently mitigate the attack by over 98.0% while benign traffic is not affected.