Availability Of Network Services Research Articles

Intrusion Detection System

Our project introduces an Integrated Intrusion Detection System (IDS) designed to bolster network and system security through a multi-faceted approach. This comprehensive IDS seamlessly combined Machine Learning, Anomaly Detection, File Scanning, and DDoS Prevention mechanisms to offer robust defense against diverse cyber threats. At the heart of the system lies the Machine Learning component, which continuously adapts to the evolving threat landscape. By analyzing network traffic and system behavior, it identifies both known and emerging threats in real-time, reducing reliance on traditional signature-based detection methods and allowing organizations to proactively stay ahead of cybercriminals. Anomaly Detection constantly monitors network and system activity, comparing it against established baselines. Any deviations from these baselines trigger alerts, facilitating swift responses to unusual activities that may indicate security breaches. File Scanning is another vital component ensuring data integrity and preventing malware infiltration or data exfiltration. It conducts thorough file analysis, checking for suspicious code, behavior, or unauthorized access, and offers continuous monitoring to detect anomalies in real-time. Additionally, our IDS includes Distributed Denial of Service (DDoS) Prevention mechanisms. By detecting and mitigating DDoS attacks, the system ensures the continuous availability of network resources and services even under intense traffic loads. This integrated approach to intrusion detection and prevention leverages Machine Learning, Honeypots, Anomaly Detection, File Scanning, and DDoS Prevention, empowering organizations to safeguard critical assets, maintain data integrity, and ensure network security in today's dynamic and perilous digital landscape. Our IDS solution serves as a valuable addition to the cybersecurity toolkit for organizations seeking comprehensive security against a wide range of threats.

SYNTROPY: TCP SYN DDoS attack detection for Software Defined Network based on Rényi entropy

The rapidly evolving landscape of network security, particularly in Software Defined Networks (SDNs), presents a critical need for efficient and adaptive DDoS attack detection methods, especially in the face of TCP SYN DDoS attacks. These attacks pose significant threats to network resources and service availability. Current state-of-the-art solutions, predominantly based on Shannon entropy, have inherent limitations, that give equal weightage to all frequency probability. This inherent assumption often leads to inadequate detection in complex and dynamic network environments, where attack patterns are increasingly sophisticated and variable. In this paper, we present a novel framework called SYNTROPY that is designed to detect TCP SYN DDoS attacks in SDN environments. The proposed SYNTROPY framework leverages Rényi entropy to effectively generalize the measurement of uncertainty in the network traffic. Unlike Shannon entropy, Rényi entropy offers the flexibility to adjust sensitivity to varying network conditions and attack patterns, thereby enhancing detection accuracy. It filters benign, flash, and suspicious traffic and employs a min–max threshold to identify attack patterns accurately. Our framework is implemented using the Ryu Controller, thus enabling seamless integration with SDN systems. The experiment is conducted to evaluate the SYNTROPY performance using the CAIDA UCSD DDoS 2007 Attack Dataset. The comparative analysis demonstrates that SYNTROPY performs better across various metrics than state-of-the-art solutions. It includes a 40% reduction in average CPU load, 59% enhancement in average detection time, 13% increase in true positives rate, 34% decrease in false negatives rate, 10% recall improvement, and 8% higher F1-Score. These promising results showcase the potential of SYNTROPY as a robust and effective solution for addressing TCP SYN DDoS attacks in SDNs.

DISTRIBUTED ARTIFICIAL INTELLIGENCE MODELS IN PERSPECTIVE COMMUNICATION NETWORKS

6G technology will create an intelligent, highly scalable, dynamic and programmable wireless network capable of serving a variety of heterogeneous wireless devices. Various 6G modules and devices will generate colossal amounts of distributed data, so post-NGN (New Generation Networks) will need to implement a number of machine learning methods that will solve significantly complicated network problems. To overcome these problems, distributed learning methods can be used, allowing devices to train models jointly, without exchanging raw data, which reduces communication costs, delays, and increases data privacy level as well. Distributed machine learning models will play an important role in 6G networks, since they have a number of advantages over centralized methods, however, the implementation of distributed algorithms in resource-constrained wireless environments can be challenging. It is important to take into account the wireless environment uncertainty associated with various disturbing factors and limited wireless (transmission power, radio frequency spectrum) and hardware resources (computing power). Consequently, it is important to choose the suitable machine learning algorithm based on the wireless environment characteristics and the resource requirements of the learning process. The article reviews the application of distributed artificial intelligence models in new generation communication networks for resource management and data processing purposes. The general algorithms and approaches of distributed machine learning, applications, methods and models are described. The article analyzes the ways in which distributed artificial intelligence models can solve various problems in communication networks, including optimizing resource use and ensuring high performance and availability of network services.

Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of Network Services

The Network Function Virtualization (NFV) framework is an enabler for the automation of Network Service (NS) management. In the context of NFV, an NS is realized by interconnecting Virtual Network Functions (VNF) using Virtual Links (VL). Availability and continuity are among the important characteristics of an NS. These characteristics depend on the availability of the VNFs and VLs composing the NS, which are usually selected at NS design time. VNFs and VLs utilize the resources of the underlying infrastructure, and their availability (partially) depends on the availability of these resources. To design an NS to fulfill availability and continuity requirements, the availability required from the resources is constrained at design time. However, the characteristics of these resources may change at runtime due to the dynamicity of NFV infrastructure. Thus, impacting the availability of the VNFs and the VLs, which in turn may impact the availability and continuity of the NS. To fulfill these requirements at runtime despite the changes in the infrastructure, the NS should be adapted. In this paper, we propose a framework for the runtime adaptation of NSs that reacts to changes and adapts the NS configuration so that it can fulfill its availability and continuity requirements during the NS lifetime. We also propose a method to develop machine learning models that are used within the framework to determine the required adjustments at runtime. We implemented the proposed framework, the method for developing the machine learning models, a testbed, and NSs to assess the feasibility and validity of our approach through experiments.

Discrimination in Online Learning During the COVID-19 Pandemic in Indonesian Higher Education

Objective: Discrimination has been significant in the COVID-19 pandemic in online learning in universities, as it affects the availability of technological devices, the training of human resources, student and parent literacy rates and institutional training.
 
 Method: Using a descriptive qualitative approach, this article aims to identify and analyze the causes of forms of discrimination occurring in Indonesian universities during the COVID-19 pandemic. Data were collected through interviews with students, teachers and parents, taking into account their characteristics, situations and particular cultures.
 
 Result: The study shows that differences in the social groups and classes of university students and mothers structured their possibilities to study online in higher education in the COVID-19 pandemic. Differences between social class have consequences on the use of online learning in the COVID-19 pandemic at the level of discrimination. Existing structural differences are an important factor in interpreting the discriminatory reality of online learning in Indonesian higher education.
 
 Conclusion: Three important factors were identified as the cause of this discrimination: limited availability of mobile devices, smartphones, tablets, laptops, signals, data packets and Internet network services; lack of technological literacy among learners and parents hampers the ability to participate; and lack of institutional training to learn online.

Региональная специфика цифровой мобилизации социального и политического взаимодействия сетевых сообществ

The digital mobilization of regional social and political interactions remains a relevant research topic. It provides obvious research innovations, new digital research algorithms, and non-trivial solutions, as well as an effective n-code measurement tool. Digital freedom means availability of network tools and services, where ordinary users can be actors of digital mobilization. In order to visualize an experimental scenario, the authors converted original data in the PolyAnalyst program to obtain thematic groups and an actor-network model for online communities of Kuzbass and the Tyumen Region. The generated dataset made it possible to consider common and different discussion topics, which were grouped by acute issues: crime, law, the military operation in Ukraine, infrastructure, etc. The study revealed some methods of user search and processing of chatbot requests. The digital tools demonstrated the regional features of the choice of online media, local resources, and topics. Online discussions give rise to new communication practices, i.e., the ability to receive, discuss, and shape relevant thematic agendas in real time, sometimes ahead of government officials and official media. This phenomenon can be explained by the availability of online community, hybrid communication technologies, repeated discussions, and alternative reliable sources.

Mitigation Services on SDN for Distributed Denial of Service and Denial of Service Attacks Using Machine Learning Techniques

Software-Defined Networking (SDN) offers an innovative model over the separation of the data plane, control plane and management plane. This separation would result in more effective network management, including cost reductions for hardware and manpower, and the ability to deliver on-demand solutions using programmable SDN approaches. As network policy and on-demand services become more impartial, SDN is becoming more popular. However, there are safety risks associated with the SDN network due to malicious floods such as Distributed Denial of Service (DDoS) attacks and Denial of Service (DoS) attacks directed at the SDN Controller, OpenFlow Virtual Switch (OVS), and end nodes, which must be addressed. Because of these assaults, network throughput is reduced, resulting in a lapse in the availability of network services and a reduction in business operations. The main emphasis of this study is on the detection and mitigation of DDoS and DoS assaults in the SDN network, which is accomplished by the use of both unsupervised and supervised learning approaches. The use of the Dynamic Access Control List (DACL) allows for the performance of mitigation operations in the SDN network, which has been implemented using the mininet. The outcome of the experiment demonstrates that malicious (DDoS and DoS) flood is reduced as a consequence of the mitigation technique.

A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization

The surge in cyber-attacks has driven demand for robust Intrusion detection systems (IDSs) to protect underlying data and sustain availability of network services. Detecting and classifying multiple type of attacks requires robust machine learning approaches that can analyze network traffic and take appropriate measures. Traffic data usually consists of redundant, irrelevant, and noisy information, which could have a negative influence on the model performance. In this paper, we propose an improved bio-inspired meta-heuristic algorithm for efficient detection and classification of multi-stage attacks. The proposed model uses a one-versus-all sub-model based technique to deal with the multi-class classification problem. Each sub-model employs an enhanced Harris Hawk optimization with extreme learning machine (ELM) as the base classifier. This hierarchy produces the best subset of features per attack, along with optimized ELMs weights, which can improve the detection rate significantly. The proposed technique was tested against various meta-heuristic algorithms and multi-class classifiers using the UNSWNB-15 dataset. In seven different types of attacks, experimental results outperformed other existing methods in terms of decreasing the crossover-error rate and obtaining the best values for the G-mean measure.

SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios

AbstractThe network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.

Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

In recent infrastructures, Internet of Things (IoT) have become an important technology for connecting various actuators and sensors over wireless networks. Due to increase in mission-critical infrastructures, we make use of these new technologies for reliable communication but their security is always not promising in terms of availability, confidentiality, integrity, and privacy of network services. Users can be compromised and vulnerable by a motivated malicious opponent unless they are not adequately protected by a robust defense. Due to this reason, an ambient intelligence approach for Intrusion Detection System (IDS) is required. In this research, ww proposed Ambient Approach based on Reinforcement Learning Integrated Deep Q-Neural Network (RL-DQN) model for WSNs and IoT in which it leverages the Markov decision process (MDP) formalism to enhance the decision performance in IDS. We deploy RL-DQN-IDS over Edge-cloud intrusion detection infrastructure in which binary attack classification of the network traffic is performed at the edge network while multi-attack classification is performed at the cloud network. To identify intrusions, we use a two-phase process that includes an initial learning phase that relies on RL, followed by a detection and classification phase that relies on DQN. We used four datasets namely UNSW-NB-15, BoTNeTIoT-L01, CICIDS2017 and IoTID20 with a smart house simulation environment configured with WSN and IoT technologies to evaluate performance. Accuracy, precision, and recall were all considered while assessing the dataset under consideration. When compared to five other machine learning models, the RL-DQN model method has demonstrated superior performance. This model outperforms the other five that were tested.

Service Function Chain Deployment Method Based on Traffic Prediction and Adaptive Virtual Network Function Scaling

With the development of network function virtualization (NFV), the resource management of service function chains (SFC) in the virtualized environment has gradually become a research hotspot. Usually, users hope that they can get the network services they want anytime and anywhere. The network service requests are dynamic and real-time, which requires that the SFC in the NFV environment can also meet the dynamically changing network service requests. In this regard, this paper proposes an SFC deployment method based on traffic prediction and adaptive virtual network function (VNF) scaling. Firstly, an improved network traffic prediction method is proposed to improve its prediction accuracy for dynamically changing network traffic. Secondly, the predicted traffic data is processed for the subsequent scaling of the VNF. Finally, an adaptive VNF scaling method is designed for the purpose of dynamic management of network virtual resources. The experimental results show that the method proposed in this paper can manage the network resources in dynamic scenarios. It can effectively improve the availability of network services, reduce the operating overhead and achieve a good optimization effect.

TD-RA policy-enforcement framework for an SDN-based IoT architecture

Internet of Things (IoT) has been suffering from increasing security threats since many years which compromise the whole network security. Automating the management of IoT devices helps in implementing security measures within communication systems. Software Defined Networking (SDN) has been introduced as a new networking approach that enables this automation. Many approaches were developed to mitigate IoT attacks in SDN-based IoT networks. Some studies investigated the prevention of flooding attacks, while others tried to cover broader attack surfaces. However, their proposed methods are time consuming and resource-exhausting as they use complex algorithms. In this paper, we propose a lightweight secure Threat Detection (TD) and Rule Automation (RA) framework namely “TD-RA” to effectively detect and mitigate different cyber-security threats in an SDN-based IoT environment. The proposed solution is composed of a Binary and Multi-class Classification Modules (BCM/MCM) for IoT threat detection and a Policy-Enforcement Module (PEM) for attack mitigation. Different machine learning methods have been implemented and compared to solve the classification problems. It is shown that for binary classification, the Decision Tree method achieves the highest accuracy which is around 98.7%, while for multi-class classification, Random Forest achieves the highest accuracy which is around 91.1%. The experimental results show that the proposed framework can successfully detect abnormal traffic and prevent IoT threats through SDN with smaller network overhead and high performance. Moreover, the overall processing time of our security modules is significantly smaller than that of existing solutions by reaching a mean value of 6 ms. This paper also introduces a large-scale architecture that comprises clusters of controllers to maintain high availability of network services. Such an integrated security approach, including detection and mitigation techniques, provides IT industries with reliable security measures that can be implemented to increase SDN-based IoT system responsiveness to different IoT attacks.

Availability and service disruption of network services: From high-level requirements to low-level configuration constraints

In the context of Network Function Virtualization (NFV), a Network Service (NS) is defined as a composition of Network Functions (NFs), which can be Virtual Network Functions (VNFs) or Physical Network Functions (PNFs). The availability of an NS is among its important characteristics. Moreover, when there is a service outage there is often a service disruption. We define the Service Disruption Time for an NS functionality as the amount of time for which the service state is lost due to service outages for a given period. The Service Data Disruption for an NS functionality is defined as the maximum amount of data lost due to a service outage. To have a comprehensive design solution considering service availability and service disruption, we propose a novel method which enhances an NS design by mapping service-level availability and disruption requirements to constraints on low-level configuration parameters, and calculates the number of required standby instances for each VNF so that the requirements can be met. Our method guarantees the availability and disruption requirements at any scaling level of the NS while minimizing the resource cost. To reduce the complexity of the complete search for the optimal configuration we also propose a heuristic algorithm and perform experiments to compare it with the complete search.

FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps identify the service rates of the devices, the availability of the system, and the number of jobs in the Industry 4.0 systems, which helps applications process data with a reasonable response time. An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application, which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time. After implementing the proposed model with different sizes of fog services in Industry 4.0 applications, FogQSYM provides a lower response time than the existing optimized response time model. It should also be noted that the average response time increases when the arrival rate increases.

Improving network performance with an integrated priority queue and weighted fair queue scheduling

<p>Quality of service (QoS) is the measure of network service availability and transmission. There are many factors influencing QoS among which one is the increasing number of network service users. The increase in the number of network service users and communication traffic causes network congestion. And the traffic congestion results in delay or packet loss and jitter variation. As a result, an organization’s network quality deteriorates and or even becomes unavailable. Therefore, to deliver a high quality network service to the users, a solution that avoids network traffic congestion is needed. In this study, the causes for network traffic congestion and the best solutions to eliminate traffic congestion in a network with congestion management and avoidance using an integrated priority queue (PQ) and weighted fair queue (WFQ) packet scheduling algorithms is proposed.</p>

FNCS: Propuesta de una plataforma de gestión de dispositivos de Red basados en RouterOS

En las organizaciones, contar con la disponibilidad delos servicios de red inalámbrica es imprescindible.Sin embargo, esta disponibilidad se ve afectada debidoprincipalmente a dos factores: la necesidad de un expertoen la configuración de los equipos y al desplazamientode dicho experto al sitio, para que este configure cadauno de los equipos que necesitan dicha configuración.Por otra parte, la empresa Mirotik dispone de un SistemaOperativo (SO) para equipos Routerboard, denominadoRouterOS (RouterBoard Operating System). EsteSO soporta la comunicación mediante los protocolostelnet, ssh, pero las aplicaciones que utilizan estosprotocolos por lo general no tienen una interfaz gráfica,además existen herramientas propietarias gráficas comowinbox y webfig para configurar estos equipos, peroesto trae consigo otro tipo de problemas como son:configuración manual de equipo en equipo, mayor usodel CPU de los dispositivos, y consumo de más anchode banda para poder gestionarlos. Para solucionarestos problemas, ha sido desarrollado una aplicacióncon interfaz web, denominada Fast Network ConfigSystem (FNCS), la cual permite gestionar y configurarde forma gráfica, remota y centralizada los dispositivoscon RouterOS, de manera mucho más eficiente que lassoluciones disponibles. Para desarrollar la aplicación,se aprovechó la API-Mikrotik que está integrada enRouterOS. Posteriormente, se comparó la velocidaden la configuración de la herramienta propuesta FNCSversus Winbox y ssh, y se comprobó que la soluciónFNCS es hasta 200 veces más rápida.

Profiling of a large-scale municipal wireless network

Pervasive connectivity is an essential underlying substrate for smart cities, leading municipalities to start programs where Wi-Fi is the fundamental building block to develop public Municipal Wireless Networks. Even though hundreds of cities around the world offer some form of Wi-Fi access, there are no widely available results regarding the network Quality of Service (QoS), user Quality of Experience (QoE), and overall utilization profile. The Municipality of São Paulo operates a free Wi-Fi Internet program in 120 public spaces called digital squares. We collected user connection data, network performance, and service availability for more than 2 years from the 120 squares and undertook experiments with video streaming in five squares. We used this unique large dataset to evaluate the impact of current admission control practices in public Wi-Fi networks on the network QoS, user QoE and service availability, also providing insights into the most common QoS/QoE issues and their causes. We also leveraged the data set to establish and verify a correlation between the number of users access in the network and specific events occurring in the area.

Resilience of communication networks to random failures and disasters: An optimization perspective

AbstractCommunication networks are subject to many challenges leading to single or multiple failures of its elements. Example failure scenarios include unintentional failures due to scheduled maintenance activities or massive failures caused by disaster‐induced events. Therefore, it is crucial to enhance the networks with redundancy and resilience mechanisms able to maintain the availability of network services after a failure. As these problems are often inherently hard to solve to optimality, the role of time‐efficient approximation schemes is essential. In this paper, we highlight the selected problems of communication networks resilience addressed in this issue of Networks.

A hybrid link protection scheme for ensuring network service availability in link-state routing networks

The internet is playing an increasingly crucial role in both personal and business activities. In addition, with the emergence of real-time, delay sensitive and mission-critical applications, stringent network availability requirement is put forward for internet service providers (ISPs). However, commonly deployed intradomain link-state routing protocols react to link failures by globally exchanging link state advertisements and recalculating routing table, inevitably causing significant forwarding discontinuity after a failure. Therefore, the loop-free criterion (LFC) approach has been widely deployed by many ISPs for coping with the single network component failure scenario in large internet backbones. The success of LFC lies in its inherent simplicity, but this comes at the expense of letting certain failure scenarios go unprotected. To achieve full failure coverage with LFC without incurring significant extra overhead, we propose a novel link protection scheme, hybrid link protection (HLP), to achieve failure resilient routing. Compared to previous schemes, HLP ensures high network availability in a more efficient way. HLP is implemented in two stages. Stage one provides an efficient LFC based method (MNP-e). The complexity of the algorithm is less than that of Dijkstra's algorithm and can provide the similar network availability with LFC. Stage two provides backup path protection (BPP) based on MNP-e, where only a minimum number of links need to be protected, using special paths and packet headers, to meet the network availability requirement. We evaluate these algorithms in a wide spread of relevant topologies, both real and synthetic, and the results reveal that HLP can achieve high network availability without introducing conspicuous overhead. HLP not only needs around 10% time of that of full protection, but also provides full protection capabilities that full protection provide.

Smart routing: Towards proactive fault handling of software-defined networks

In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic in both the industrial and academic sectors. Software-defined networking offers numerous benefits against legacy networking systems by simplifying the process of network management through reducing the cost of network configurations. Currently, data plane fault management is limited to two mechanisms: proactive and reactive. These fault management and recovery techniques are activated only after a failure occurrence and hence packet loss is highly likely to occur. This is due to convergence time where new network paths will need to be allocated in order to forward the affected traffic rather than drop it. Such convergence leads to temporary service disruption and unavailability. Practically, not only the speed of recovery mechanisms affects the convergence, but also the delay caused by the process of failure detection. In this paper, we define a new approach for data plane fault management in software-defined networks where the goal is to eliminate the convergence process altogether rather than accelerate the failure detection and recovery. We propose a new framework, called Smart Routing, which allows the network controller to receive forewarning signs on failures and hence avoid risky paths before the failure incidents occur. The proposed approach aims to decrease service disruption, which in turn increases network service availability. We validate our framework through a set of experiments that demonstrate how the underlying model runs and its impact on improving service availability. We take as example of the applicability of the new framework three types of topologies covering real and simulated networks.