Networking Architecture Research Articles

Self-protected content for information-centric networking architectures using verifiable credentials

Self-protected content for information-centric networking architectures using verifiable credentials

An Analysis of Software Defined Networks and Possibilities of Network Attacks

This article focusses on a rapidly evolving networking architecture known as Software Defined Networking (SDN) and the possibilities of hazards in the network. This architecture introduces decoupled infrastructure, which establishes customization in the networking system hence making it easy to manage, troubleshoot and configure. This paper focusses on the different aspects of the architecture leaving it an intermediate working in between scholarly application, adding on the elements such as security lapses, security behaviors, general security, programmability and design. In this paper, different points of weakness of the architecture have been evaluates, including the attack vector in every plane. This paper ends with a presentation for futuristic studies on the implications of attacks and potential solutions.

A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

ELITE: An Intelligent Digital Twin-based Hierarchical Routing Scheme for Softwarized Vehicular Networks

Software-Defined Vehicular Network (SDVN) is a networking architecture that can provide centralized control for vehicular networks. However, the design for routing policies in SDVNs is generally influenced by several limitations, such as frequent topological changes, complex service requests, and long model training time. Intelligent Digital Twin-based Software-Defined Vehicular Networks (IDT-SDVN) can overcome these weaknesses and maximize the advantages of the conventional SDVN architecture by enabling the controller to construct virtual network spaces and provide virtual instances of corresponding physical objects within the Digital Twin (DT). In this paper, we propose a junction-based hierarchical routing scheme in IDT-SDVN, namely, intelligent digital twin hierarchical (ELITE) routing. The proposed scheme is conducted in four phases: policy training and generation in the virtual network, and deployment and relay selection in physical networks. First, the policy learning phase employs several parallel agents in DT networks and derives multiple single-target policies. Second, the generation phase combines the learned policies and generates new policies based on complex communication requirements. Third, the deployment phase selects the most suitable generated policy according to the real-time network status and message types. A road path is calculated by the controller based on the selected policy and then sent to the requester vehicle. Finally, the relay selection phase is utilized to determine relay vehicles in a hop-by-hop process along the selected path. Simulation results demonstrate that ELITE achieves substantial improvements in terms of packet delivery ratio, end-to-end delay, and communication overhead compared with its counterparts.

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.

Detection of cyber-attacks in network control planes using Hidden Markov Model

Software Defined Networking (SDN) is a networking architecture within the control is centralized through a software-based controller. Like Cyber-Physical Systems manager, this centralization eases the support of advanced application. Being a single point of attack makes the controller a preferred target in case of attack. To enhance the control plane against cyber-attacks, an observer is introduced and is in charge of the detection of cyber-attacks on the nominal controller. In this objective, a detection of anomalies method in the activity of the control is proposed. This activity is defined as the events at the interface of communication between the controller and the network plant. In this paper, a non-deterministic control is considered which means that the decisions are stochastic. Hence, a probabilistic approach is proposed which aims to evaluate the deviation of the likelihood of the sequence of decisions taken by the controller. The formalism used to determine the likelihood is the Hidden Markov Model which permits to infer over the internal states of the controller through the observations. This method is discussed on a network case study.

Smart architecture for software-defined networking

Software-defined networks (SDN) seek to solve the problems in current network schemes by simplifying their management through their reprogrammability and accessibility to the overall network infrastructure. One aspect to improve in SDN-based schemes is the precise classification of your traffic load, this can improve various aspects such as quality of service, dynamic access control, prioritized random access, among others. This research aims to propose a conceptual architecture of SDN and evaluate different machine learning methods for traffic classification. To this end, SDN architectures are analyzed and different modules are proposed to strengthen their management with the help of low computational cost classifiers. The architecture proposes the following main modules: Capture network traces module, Learning Engine module, and ML-model and Flow classifier. To determine the model to be used in the Learning Engine and Flow classifier modules, different classifiers were evaluated using a database of network traffic, as a result, it was determined that the gradient boosting algorithm is the most suitable to be integrated with the proposed SDN architecture.

Producer mobility support in information-centric networks: research background and open issues

In recent times, an interesting innovation of information-centric networking (ICN) architecture has been introduced to replace the old fashion host-centric communication architecture with content-centric architecture. With the instant growth of mobile devices and mobile data traffic, the mobility issue in the current IP-based network is growing rapidly and is facing many inefficiencies such as unsupported mobility and data delivery. The unsupported mobility causes failure of the communication system and inaccessibility in the network services. The unsupported mobility stimulates several complications and issues in the network such as unavailability of content, denial of service, interest packets loss, and excessive burden in the network. Further, unsupported mobility affects the routing and forwarding plane in the network and terminates the content transmission to its consumers. Thus, this paper will critically explore this issue. It will present a strong foundation of the producer mobility support architecture, research challenges, previous solutions, and open research issues. Along with this discussion, a research roadmap will be delineated.

A comparison of hybrid deep learning models for pneumonia diagnosis from chest radiograms

It is estimated that about 5–7 million children die from pneumonia every year. Pneumonia affects a large proportion of the world's population, approximately 7%, and is one of the most common respiratory illnesses in humans. Chest X-Ray is the most frequent way of diagnosing pneumonia. However, in certain countries, there is a scarcity of qualified radiologists, and the detection procedure performed by radiologists is not very accurate and quite difficult. The detection accuracy should be improved. So, this method is proposed to reduce the complexity of diagnosis and a helping hand of the radiologists. A Convolutional Neural Networking (CNN) architecture with 22 layers was created for this proposed method and three distinct machine learning techniques were used to extract and classify the CNN model's learned features. Support Vector Machine, Random Forest Classifier, and K-Nearest Neighbor. To add variety to the current dataset, certain data augmentation techniques were utilized. Overfitting was avoided by using a regularizer in the first dense layer and also data augmentation is utilized. The dataset used was Mendeley Data v2 having a total of 5856 chest x-ray images in both Pneumonia and Normal Class. After the classification of the RF, KNN and SVM classifiers differently using CNN model trained features, the accuracy obtained respectively were 99.52%, 96.55% and 97.32%. This proposed CNN-RF hybrid method is comparable with other existing conventional methods having accuracy of 99.52% and the AUC score of 98.7%.

ESMD-Flow: An intelligent flow forwarding scheme with endogenous security based on Mimic defense in space-air-ground integrated network

The Space-Air-Ground Integrated Network (SAGIN) realizes the integration of space, air, and ground networks, obtaining the global communication coverage. Software-Defined Networking (SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service (QoS). However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense (ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multiprotocol forwarding strategy for realizing the multiprotocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on manually configuring the policies. As the massive traffic from heterogeneous networks increases, conventional solutions are ineffective due to the lack of automation and human interference. This necessitates producing a detection solution, more effective than traditional ones, to ensure SDN security, resiliency, and availability. This paper addresses this problem by proposing a deep learning (DL)-based ensemble solution for efficient DDoS attack detection in SDN. Four hybrid models are presented by adopting three ensemble techniques and different DL architectures, namely convolutional neural network, long short-term memory, and gated recurrent unit, to improve the SDN traffic classification. The experimentation was conducted on the benchmark flow-based dataset CICIDS2017. High detection accuracy (99.77%) with a small number of flow-based features was achieved by our ensemble model, as our experimental results will demonstrate. The proposed solution was evaluated by several standard assessment matrices and by comparing against other state-of-the-art algorithms from the network security literature.

Toward the Protection of IoT Networks: Introducing the LATAM-DDoS-IoT Dataset

Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks. However, Intrusion Detection Systems based on Artificial Intelligence, as a defense mechanism, need robust data sources to achieve strong generalization levels from the knowledge domain of interest. Therefore, in this research we present the LATAM-DDoS-IoT dataset, which results from a collaboration among Aligo, Universidad de Antioquia, and Tecnologico de Monterrey. The LATAM-DDoS-IoT dataset includes attack traffic to physical Internet of Things devices and normal traffic from real external users consuming actual services from Aligo’s production network. We also compare this new dataset with the Bot-IoT dataset, as the latter is a collection of data used in recent approaches to create detection systems in the Internet of Things domain. Furthermore, we build a smart anomaly-based Intrusion Detection System from our new dataset, training Decision Tree and Multi-layer Perceptron models, for later deployment and evaluation on a Software Defined Networking architecture with physical and virtual components. Before deployment, we obtained an average accuracy of 99.967% and 98.872% with our new dataset’s balanced denial of service and distributed denial of service versions. After deployment, we show that our Intrusion Detection System does not misclassify legitimate traffic and detects more than 90% of the attacks.

Immersive Technologies in 5G-Enabled Applications: Some Technical Challenges of the Novel Usage Models

5G next-generation networking paradigm with its envisioned capacity, coverage, and data transfer rates provide a developmental field for novel applications scenarios. Virtual, Mixed, and Augmented Reality will play a key role as visualization, interaction, and information delivery platforms. The recent hardware and software developments in immersive technologies including AR, VR and MR in terms of the commercial availability of advanced headsets equipped with XR-accelerated processing units and Software Development Kits (SDKs) are significantly increasing the penetration of such devices for entertainment, corporate and industrial use. This trend creates next-generation usage models which rise serious technical challenges within all networking and software architecture levels to support the immersive digital transformation. The focus of this paper is to detect, discuss and propose system development approaches and architectures for successful integration of the immersive technologies in the future information and communication concepts like Tactile Internet and Internet of Skills.

ESRA: Energy soaring-based routing algorithm for IoT applications in software-defined wireless sensor networks

Software-defined wireless sensor networking is an emerging networking architecture envisioned to play a critical role in the looming internet of things paradigm. Since energy is a scarce resource in wireless sensor networks, many energy-efficient routing algorithms were proposed to enhance the network lifetime. However, most of these algorithms lack network stability and reliability in the presence of dead nodes. This paper presents ESRA: Energy Soaring-based Routing Algorithm for IoT Applications in Software-Defined Wireless Sensor Networks, specifically for monitoring environment to address this shortcoming. The proposed ESRA algorithm efficiently selects the network cluster heads to be considered for solving the controller placement problem, intending to achieve network reliability and stability and enhance the network lifetime. The selection of controllers among the cluster heads is formulated as an NP-hard problem, considering the residual energy of the cluster heads, their spatial distance to the sink, and their load or density. To tackle this NP-hard problem, genetic algorithm is adopted to optimize the network lifetime, throughput, latency, and network reliability in the presence of different percentages of dead nodes. Simulation results showed that ESRA outperforms other three state-of-the art algorithms in terms of network lifetime and throughput by 15%, 20%, and 25%, in terms of energy savings by 10%, 20%, and 25%, and in terms of delay by 10%, 15%, and 20%. We also applied the proposed scheme on real networks adopted from the internet topology zoo, which showed promising results compared to other existing works.

Optimized Routing Protocols- Enhancement in Mobile Adhoc Network

The recent developments in wireless technology have piqued academics' curiosity. Mobile Adhoc Networks (MANET) is a fast-growing networking architecture used in emergency, disaster, and tactical operations applications. It covers a wide range of applications with some degradation due to its flexible and expandable nature. Because routing is the primary function of wireless networks, routing protocol design is critical in Adhoc networks. Various methods have been used to improve the performance of current routing protocols in different categories. Technical advances in routing protocols have recently been achieved, combined with optimization approaches to improve the overall performance of MANETs routing protocols. This research initially looked at several sorts of routing protocols before going through their variations for energy economy and latency reduction. The previous protocols, which were created utilizing optimization methods, will then illustrate the current requirement. Finally, the review analysis delves into many aspects such as commonly used protocols, optimization techniques, QoS parameters, performance metrics, and future paths.

ImPACT: A networked service architecture for safe sharing of restricted data

In this paper we describe an architecture developed and prototyped in the course of the NSF-funded project called ImPACT—Infrastructure for Privacy-Assured CompuTations. This architecture addresses the common problems that arise from the need to securely store, control access to and process privacy-restricted data in a multi-institutional, multi-stakeholder setting. Specifically the architecture includes several components—a way to publicly advertise a limited set of data attributes without exposing the sensitive data itself; a set of mechanisms for a data owner to specify and automatically enforce complex data-access policies commonly expressed today as Data Use Agreements (DUAs); a way to securely collect digital attestations from multiple stakeholders to satisfy those policies; and a reproducible template to deploy secure processing enclaves in which groups of researchers can analyze the data in a way that complies with data owner policies using the tools of their choice. The paper describes the architecture and its instantiation in a prototype, providing a performance evaluation of several components.

A mobility-compliant publish–subscribe system for an information-centric Internet of Things

The Information centric networking paradigm has proven particularly useful for the constrained Internet of Things (IoT), in which nodes are challenged by end-to-end communication without network assistance. This work focuses on the interaction between possibly mobile sensors and actuators in such IoT regimes which deploy the Named-Data Networking (NDN) architecture. Constrained nodes in interactive scenarios need to be highly responsive but can only manage limited control state. We argue that the request-driven NDN networking paradigm, which prevents pushing of unsolicited data, should be preserved to confine the attack surface, whereas unsolicited link-local signaling can accelerate responses without sacrificing security.In this paper, we contribute HoP-and-Pull (HoPP), a robust publish–subscribe scheme for typical IoT scenarios that targets low-power and lossy wireless networks running hundreds of resource constrained devices at intermittent connectivity. Our approach limits in-memory forwarding state to a minimum and naturally supports producer mobility, a temporary partitioning of networks, data aggregation on intermediary hops, and near real-time reactivity. We thoroughly evaluate the protocol by experiments in a realistic, large testbed with varying numbers of constrained devices, each interconnected via IEEE 802.15.4 wireless LoWPANs. We compare HoPP with common ICN pub–sub and mobility schemes as well as with basic MIPv6 and anchor-based multicast mobility. Implementations are built on CCN-lite with RIOT and support experiments using various single- and multi-hop scenarios.

Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System

Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.

An investigation on fronthaul and millimeter wave technologies for 5G

.The growing demand for communications and the emergence of new applications have put the capacity, transmission rate and latency of communications systems to a more severe test. In this context, 5G communication technology has emerged. 5G applications are divided into three typical application scenarios: eMBB, URLLC and mMTC. In order to support these applications, the key technologies of 5G need to be studied in depth. This paper firstly investigates the networking architecture of 5G bearer networks, and carries out a detailed analysis and comparison of 5G fronthaul technology, including the optical fibre direct connection solution, passive WDM solution, active WDM/OTN solution and WDM-PON solution. Secondly, this paper introduces 5G millimetre wave technology to achieve large capacity and high spectral efficiency transmission, including direct intensity modulation method, external modulation method, optical heterodyne method, optical injection locking method and optical phase-locked loop method. Finally, this paper provides an outlook on 5G fronthaul technology and millimetre wave technology.

Down the Rabbit Hole: Fostering Active Learning through Guided Exploration of a SCADA Cyber Range

Prior experience from the authors has shown that a heavily theoretical approach for cybersecurity training has multiple shortcomings, mostly due to the demanding and diversified nature of the prerequisites, often involving concepts about operating system design, networking and computer architecture, among others. In such circumstances, the quest for trainee engagement often turns into a delicate balancing act between managing their expectations and providing an adequate progression path. In this perspective, hands-on exercises and contact with high-fidelity environments play a vital part in fostering interest and promoting a rewarding learning experience. Making this possible requires having the ability to design and deploy different use case training scenarios in a flexible way, tailored to the specific needs of classroom-based, blended or e-learning teaching models. This paper presents a flexible framework for the creation of laboratory and cyber range environments for training purposes, detailing the development, implementation and exploration of a cyber range scenario, within the scope of a course on cyber-physical systems security. Moreover, the course structure, curricular aspects and teaching methods are also detailed, as well as the feedback obtained from the students.