Network Paradigm Research Articles

A deep learning technique to detect distributed denial of service attacks in software-defined networks

Software-Defined Network (SDN) is an established networking paradigm that separates the control plane from the data plane. It has central network control, and programmability facilities, therefore SDN can improve network flexibility, management, performance, and scalability. The programmability and control centralization of SDN have improved network functions but also exposed it to security challenges such as Distributed Denial of Service (DDoS) attacks that target both control and data planes. This paper proposes an effective detection technique against DDoS attack in SDN control plane and data plane. For the control plane, the technique detects DDoS attacks through a Deep Learning (DL) model using new features extracted from traffic statistics. A DL method (AE-BGRU) for DDoS detection uses Autoencoder (AE) with Bidirectional Gated Recurrent Unit (BGRU). The proposed features for the control plane include unknown IP destination address, packets inter-arrival time, Transport layer protocol (TLP) header, and Type of service (ToS) header. For the data plane, the technique tracks the switch's average arrival bit rate with an unknown destination address in the data plane. Then, the technique detects DDoS attacks through a DL-based model which also uses AE with BGRU. The proposed features in the data plane include the switch's stored capacity, the average rate of packets with unknown destination addresses, the IP Options header, and the average number of flows. The dataset is generated from feature extraction and computations from normal and attack packets and used with the classifier. Also, additional Machine Learning (ML) methods are used to enhance the detection process. If the model detects an attack, the technique mitigates DDoS effects by updating the user's trust value and blocking suspicious senders based on the trust value. The experimental results proved that compared to related techniques, the suggested method had a higher accuracy and lower false alarm rate.

Routing approaches in named data network: a survey and emerging research challenges

Named Data Networking (NDN) has emerged as a promising information-centric networking paradigm that addresses the limitations of the traditional IP-based Internet architecture. The core principle of NDN relies on content naming instead of host addressing, to provide efficient, secure, and scalable content delivery. Routing is a critical component of NDN and is responsible for discovering and maintaining optimal paths to named content. This paper presents a comprehensive review of routing techniques in NDN, focusing on the design principles, algorithms, and performance metrics, especially in wired network architecture. We first summarize the NDN architecture and discuss its key components. We then delve into the fundamental routing concepts in NDN and categorize and examine various routing techniques, including link state, distance vector, and centralized approaches based on Software Defined Network. We also summarize the relevant research efforts proposed to address NDN routing challenges by focusing more on wired network architecture. Finally, we identify open research issues and future directions in NDN routing, emphasizing the need for scalable, efficient, and secure routing techniques that can fulfill the growing demands of the modern Internet. In conclusion, this review serves as a valuable reference for researchers and practitioners in NDN, offering a comprehensive understanding of the current state-of-the-art routing techniques, limitations, and potential future advancements.

A Vision of 6th Generation of Fixed Networks (F6G): Challenges and Proposed Directions

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services and exerting a major impact upon all aspects of human activity, be it social interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity, and connectivity. F6G networks differ from those of previous generations (F1G–F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches, etc., such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories, namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose (a) the use of all-optical processing to mitigate performance barriers of analog-to-digital and digital-to-analog converters (ADC/DAC) and (b) the exploitation of optical multi-band transmission, space division-multiplexing, and the adoption of more efficient modulation formats.

AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks.

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches.

Securing NFV/SDN IoT Using VNFs Over a Compute-Intensive Hardware Resource in NFVI

The Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are network paradigms for flexibly deploying future networks while guaranteeing security service requirements. This work designs Virtual Network Functions (VNFs) through a Compute-Intensive (CI) hardware resource in Network Function Virtualization Infrastructure (NFVI). The proposed NFVI is characterized by a multi-channel cryptosystem, which can be virtualized as a plurality of VNFs, that is, crypto engines, and each crypto engine is logically dedicated to an NFV/SDN Internet of Things (IoT) device, which does not have or has limited security capability owing to resource constraints. To enhance the performance of a cryptosystem, the accelerator circuit is often deeply pipelined and unrolled. However, to fulfill the popular feedback operation modes, the throughput of the pipelined and unrolled cryptosystem that implements a block cipher, say Advanced Encryption Standard (AES), can deteriorate even lower than that without these techniques. To solve this problem, we design a pipelined and unrolled multichannel cryptosystem, which can be integrated into NFVI edge servers, with feedback operation mode for the NFV/SDN IoT. As a result, the combinational logics of a block cipher with feedback can be shared by plenty of IoT devices to enhance the hardware efficiency as well. Moreover, in addition to briefly review several AES designs, the fastest AES design is derived by shortening its critical path to only a logic gate of multiplexer or Exculsive OR (XOR) gate.

An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks

Software-defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and provides programmable network management. The control plane within SDN is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts performance of SDN. In this work, we utilize clustering algorithms to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. Initially, using the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the optimal location for placing the controller within each cluster. We implement the proposed work in the Mininet emulator with POX as the SDN controller. Furthermore, we compare our proposed approach to existing work in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.

Survey on 5G Security Challenges in Device to Device Communication

Abstract: Mobile communication networks have evolved from first generation to fifth generation as a result of a continuing requirement for enhancing network’s capacity to satisfy the expanding needs of customers (5G). In following years, the no. of linked devices is estimated to grow to over fifty billion, according to estimates. The heterogeneity of such a vast number of connections is expected, necessitating faster data rates, shorter latency, more system capacity, and higher throughput. Mobile networks will have to be expanded to meet up with increasing expectations. In order to meet consumers' expanding expectations and make efficient use of limited sources, D2D communication is regarded as an innovative technique for existing and future wireless networks. It permits users to connect with one another, resulting in increased energy efficiency and system throughput. This study provides a thorough examination of D2D communications, as well as the security challenges that D2D must address in order to become a viable wireless network paradigm.

Opt_att-GANC: Fusion of CT and PET images using an optimal attention-based generative adversarial network with classifier for lung cancer classification

Lung cancer incidence and mortality continue to rise rapidly around the world. According to the American Cancer Society, the five-year survivability for individuals in the metastasis phases is significantly lower, highlighting the importance of early lung cancer diagnosis for effective therapy and improved quality of life. To achieve this, it is crucial to combine PET’s sensitivity for recognizing abnormal regions with CT’s anatomical localization for evaluating PET-CT images in computer-assisted detection implementations. Current PET-CT image evaluation methods either run each modality independently or aggregate the data from both, but they often overlook the fact that different visual features encode different types of data from different modalities. For instance, high atypical PET uptake within the lungs is more crucial for identifying tumors compared to physical PET uptake in the heart. To address the challenges of fine-grained issues during feature extraction and fusion, we propose an interpretable deep learning-based solution for lung cancer diagnosis using CT and PET images. This involves building an Optimal Adversarial Network for merging and an Optimal Attention-based Generative Adversarial Network with Classifier (Opt_att-GANC) to augment the classification of the existence and nonexistence of lung cancer based on extracted features. The performance of the Opt_att-GANC is compared with existing methodologies like global-feature encoding U-Net (GEU-Net), 3D Dense-Net, and 3D Convolutional Neural Network Technique (3D-CNN). Results show that the proposed Opt_att-GANC achieves an F1-score of 67.08%, 93.74% accuracy, 92% precision, 92.1% recall, and 93.74% recall. The prospective study aims to enhance the precision degree with reduced duration by incorporating an ensemble neural network paradigm for feature extraction.

Fairness-aware task offloading and load balancing with delay constraints for Power Internet of Things

The rapid development of the Power Internet of Things (PIoT) enables power smart sensing devices to offload their computation tasks to nearby edges. However, due to the increasing demand for computing and the unbalanced spatial distribution of devices, it is imperative to seek cooperative computing and task scheduling optimization schemes at the edge for PIoT. In this paper, we develop a two-tier cooperative edge network paradigm in PIoT. Then, we define a novel fairness indicator based on the Theil index to measure the allocation balance of the system. We also formulate a fairness and delay guaranteed (FDG) task offloading and load balancing optimization problem, which aims to minimize the allocation difference of the edge network while satisfying the delay constraints for multiple tasks in PIoT. Moreover, we develop a Lyapunov optimization and whale optimization algorithm (LWOA) to solve the problem. The simulation results demonstrate that for two types of typical tasks in PIoT, compared with the NonB scheme, the proposed FDG scheme decreases the time-averaged allocation difference of the system by 10% and 35%, the time-averaged allocation difference within subsystems by 5% and 6%, the time-averaged delay by approximately 5% and 7%, and the time-averaged queue backlog by approximately 30% and 40%. Research, both theoretical and experimental, has demonstrated that cooperation at the edge can significantly improve the performance of PIoT.

Epistemic modeling uncertainty of rapid neural network ensembles for adaptive learning

Emulator embedded neural networks, which are closely related to physics informed neural networks, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network model are trained with different random initializations. The ensemble of model realizations is used to assess epistemic modeling uncertainty caused by a lack of training samples. This uncertainty estimation is crucial information for successful goal-oriented adaptive learning in an aerospace system design exploration. However, the costs of training the ensemble models often become prohibitive and pose a computational challenge, especially when the models are not trained in parallel during adaptive learning. In this work, a new type of emulator embedded neural network is presented using the rapid neural network paradigm. Unlike the conventional neural network training that optimizes the weights and biases of all the network layers by using gradient-based backpropagation, rapid neural network training adjusts only the last layer connection weights by applying a linear regression technique. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy. The proposed method is demonstrated on multiple analytical examples, as well as an aerospace flight parameter study of a generic hypersonic vehicle.

Dynamics, uncertainty and control in circular supply chains

This work introduces a prototype dynamic model of a circular supply chain based on the state-task network paradigm, and a framework for characterizing the effect of uncertainty on the system dynamics. Processing tasks are represented using transfer functions coupled with saturation functions to capture physical limitations such as inventory depletion. Physically-motivated uncertain parameters are used in the aforementioned transfer functions. Closed-form expressions are derived for the mean and variance of inventories of material in different states, showing that the variance of inventory increases over time. The results are validated via extensive simulations, complemented by an example implementation of model predictive control for tracking material inventory targets and reducing variance.

Modeling DDOS attacks in sdn and detection using random forest classifier

ABSTRACT A Software-defined network paradigm provides flexibility and programmability to deal with the growing users of future networks. As a result of the centralized control attribute, it could be regarded as a single point of failure that is vulnerable to various forms of attacks, such as Distributed denial of service (DDOS) attacks. This study attempts to show a mathematical representation of DDOS attacks in SDN, together with how some five-tuple features contribute to the attacks. The studied features were used to detect DDOS using a random forest classifier. The result shows 96.3% detection accuracy and 96.45% precision.

Security and risk analysis in the cloud with software defined networking architecture

Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.

An Adaptive Mode Selection Mechanism for Hybrid Active and Passive Communications

In this paper, we propose an adaptive mode selection algorithm for a hybrid active and passive communication system, where the communication paradigm of the wireless-powered communication network (WPCN) is referred to as the active mode, while ambient backscatter (AB) is applied for the passive mode. Specifically, the proposed algorithm switches between the active and passive modes by simultaneously considering both energy harvesting and signal-to-noise ratio (SNR) conditions. Besides, to further improve the system's reliability, we adopt the rate-adaptive technique in passive mode, allowing devices to choose an appropriate rate for data transfer when performing backscattering. We also introduce analytical models to characterize the performance of the proposed mode selection algorithm. Simulation results validate the analysis and indicate that the proposed mode selection algorithm has the advantage in terms of both success probability and average throughput.

Software defined fog platform

<p><span lang="EN-US">In recent years, the number of end users connected to the internet of things (IoT) has increased, and we have witnessed the emergence of the cloud computing paradigm. These users utilize network resources to meet their quality of service (QoS) requirements, but traditional networks are not configured to backing maximum of scalability, real-time data transfer, and dynamism, resulting in numerous challenges. This research presents a new platform of IoT architecture that adds the benefits of two new technologies: software-defined networking and fog paradigm. Software-defined networking (SDN) refers to a centralized control layer of the network that enables sophisticated methods for traffic control and resource allocation. So, fog paradigm allows for data to be analyzed and managed at the edge of the network, making it suitable for tasks that require low and predictable delay. Thus, this research provides an in-depth view of the platform organize and performance of its base ingredients, as well as the potential uses of the suggested platform in various applications.</span></p>

A QoS-Aware Energy-Efficient Chimp Optimization Routing Protocol with Efficient Sensor Node Deployment Strategy in Underwater Acoustic Sensor Network

As an extension of wireless sensor networks in the underwater environment, Underwater Acoustic Sensor Networks (UASNs) have led to a broad consideration of academicians. One of the problems that lower UWSN effectiveness in terms of network lifespan is premature energy depletion. It might be caused by the network nodes using different amounts of energy. In UASNs, the effectiveness and dependability of data transfer remain so adverse because of the intricate underwater environment in diverse ocean applications like surveilling atypical submarine oil pipelines. Inspired by the significance of UASNs’ quality of service in several implementations, this study proffers a metaheuristic optimization algorithm (AG) called Chimp Optimization-based Routing Protocol (CH-ORP) for UASNs obtaining intricate features of underwater (UW) medium into concern like 3D changing topology, high propagation delay, node mobility, and density, and, also, cluster head nodes’ rotation mechanism. Initially, the entire network (NW) paradigm has been considered as a three-dimensional cube out of a grid point of view, and the three-dimensional cube has been split into several little cubes by employing Delaunay Triangulation. The optimization has been carried out for lessening node failure rate and NW energy consumption rate by ideally placing the sensor nodes in UW acoustic communication. The NW topology’s steadiness has been assured by the AG, and this optimizes the node redeployment scheme by computing the fitness function for all nodes. The proffered AG’s simulation substantiations have been performed for exhibiting the CH-ORP’s efficiency that executes finer when compared to the advanced methodologies concerning energy efficiency, reliability, and end-to-end delay. It has been found that the proposed Ch-ORP achieves 698 packets received with 28% of energy consumption, 156-sec Network delay, 257 packet loss, 97.23% of PDR, and 1256 Mbps of Network throughput.

RIS-Aided Cell-Free MIMO System: Perfect and Imperfect CSI Design for Energy Efficiency

Thanks to the development of the 6th generation mobile network that makes it possible for us to move towards an intelligent ubiquitous information society, among which some novel technologies represented by cell-free network has also attracted widespread academic attention. Cell-free network has brought distinguished gains to the network capacity with its strong ability against inter-cell interference. Unfortunately, further improvement demands more base stations (BSs) to be settled, which incurs steep cost increase. To address this issue, reconfigurable intelligent surface (RIS) with low cost and power consumption is introduced in this paper to replace some of the trivial BSs in the system, then, a RIS-aided cell-free network paradigm is formulated. Our objective is to solve the weighted sumrate (WSR) maximization problem by jointly optimizing the beamforming design at BSs and the phase shift of RISs. Due to the non-convexity of the formulated problem, this paper investigates a joint optimizing scheme based on block coordinate descent (BCD) method. Subsequently, on account of the majority of the precious work reposed perfect channel state information (CSI) setup for the ultimate performance, this paper also extends the proposed algorithm to the case wherein CSI is imperfect by utilizing successive convex approximation (SCA). Finally, simulation results demonstrate that the proposed scheme shows great performance and robustness in perfect CSI scenario as well as the imperfect ones.

Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners Under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams in steady state, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-1/e$</tex-math> </inline-formula> factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.

Kaki: Efficient Concurrent Update Synthesis for SDN

Modern computer networks based on the software-defined networking (SDN) paradigm are becoming increasingly complex and often require frequent configuration changes in order to react to traffic fluctuations. It is essential that forwarding policies are preserved not only before and after the configuration update but also at any moment during the inherently distributed execution of such an update. We present Kaki, a Petri game based tool for automatic synthesis of switch batches which can be updated in parallel without violating a given (regular) forwarding policy like waypointing or service chaining. Kaki guarantees to find the minimum number of concurrent batches and supports both splittable and nonsplittable flow forwarding. In order to achieve optimal performance, we introduce two novel optimisation techniques based on static analysis: decomposition into independent subproblems and identification of switches that can be collectively updated in the same batch. These techniques considerably improve the performance of our tool Kaki, relying on TAPAAL’s verification engine for Petri games as its backend. Experiments on a large benchmark of real networks from the Internet Topology Zoo database demonstrate that Kaki outperforms the state-of-the-art tools Netstack and FLIP. Kaki computes concurrent update synthesis significantly faster than Netstack and compared to FLIP, it provides shorter (and provably optimal) concurrent update sequences at similar runtimes.

Construction Machinery Monitoring System using LoRa WAN

Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities, precision agriculture, etc. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of IoT applications, i.e., low cost, large scale deployment and high energy efficiency. Among all available LPWAN technologies, LoRa networking has attracted much attention from both academia and industry, since it specifies an open standard and allows us to build autonomous. This paper focuses on leveraging LoRa Technology for Construction Machinery Monitoring and Tracking System to increase productivity and faster execution of construction Projects.