DETECTION AND MITIGATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS ON NETWORK ARCHITECTURE SOFTWARE DEFINED NETWORKING USING THE NAIVE BAYES ALGORITHM

DDoS attacks are a form of attack carried out by sending packets continuously to machines and even computer networks. This attack will result in a machine or network resources that cannot be accessed or used by users. DDoS attacks usually originate from several machines operated by users or by bots, whereas Dos attacks are carried out by one person or one system. In this study, the term to be used is the term DDoS to represent a DoS or DDoS attack. In the network world, Software Defined Network (SDN) is a promising paradigm. SDN separates the control plane from forwarding plane to improve network programmability and network management. As part of the network, SDN is not spared from DDoS attacks. In this study, we use the naïve Bayes algorithm as a method to detect DDoS attacks on the Software Defined Network network architecture

Dynamic Packet Duplication for Industrial URLLC

The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources.

Outage Constrained Design in NOMA-Based D2D Offloading Systems

Non-orthogonal multiple access (NOMA) is a new multiple access method that has been considered in 5G cellular communications in recent years, and can provide better throughput than traditional orthogonal multiple access (OMA) to save communication bandwidth. Device-to-device (D2D) communication, as a key technology of 5G, can reuse network resources to improve the spectrum utilization of the entire communication network. Combining NOMA technology with D2D is an effective solution to improve mobile edge computing (MEC) communication throughput and user access density. Considering the estimation error of channel, we investigate the power of the transmit nodes optimization problem of NOMA-based D2D networks under the rates outage probability (OP) constraints of all single users. Specifically, under the channel statistical error model, the total system transmit power is minimized with the rate OP constraint of a single device. Unfortunately, the problem presented is thorny and non-convex. After equivalent transformation of the rate OP constraints by the Bernstein inequality, an algorithm based on semi-definite relaxation (SDR) can efficiently solve this challenging non-convex problem. Numerical results show that the channel estimation error increases the power consumption of the system. We also compare NOMA with the OMA mode, and the numerical results show that the D2D offloading systems based on NOMA are superior to OMA.

A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning

Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.

Intelligent Resource Allocation for Massive Internet of Things Traffic

The Internet of Things (IoT) is considered a major trend in computing and in specific areas such as Smart Cities, Smart Grid, Industry 4.0, and mobile applications based on 5G. Typically, this set of technologies requires the orchestration of heterogeneous resources that are allocated over distinct infrastructures such as Cloud Computing, Cloud of Things, Datacenters, and network backbones. Consistent with this demand, the PSIoT-Orch framework was designed to orchestrate massive IoT traffic and to allocate network resources between Aggregators and Consumers in a Publish / Subscribe strategy. This dissertation aims to build an intelligent module for PSIoT-Orch that is capable of handling data types with different transmission requirements, aiming at the efficient use of a limited communication link. The proposed component uses Reinforcement Learning, more specifically, the SARSA algorithm to dynamically adjust the available bandwidth according to transmission priority. This solution, named PSIoT-SARSA, is validated in a simulation environment under the statistical methods of Analysis of Variance and Response Surface Analysis and, at the end of the study, it is observed that it obtained promising results. The contributions are focused on gathering an approach that allows allocating bandwidth in an intelligent way, allowing efficient scheduling of the IoT flow, in the scenario of the Smart Grid.

A Fat Client OS Architecture Supported by Semi-network Resources

Purpose–This study is to explore a way toretainthe strengths and eliminatethe weaknesses of the existingarchitecture oflocal OS and cloud OS,then create an innovativeone, which is referredto as semi-network OS architecture.Method–The elements of semi-network OS architecture includes networkresources, localresources, and semi-mobile hardware resources; among them, networkresources are the expanded portionof OS, which is used to ensure the scalability of OS; local resources are the base portion of OS, which is used to ensure the stability of local computing, as well as the autonomy of user operations; the semi-mobile hardware resource is OSPU, which is used to ensure the positioning and security of dataflow.Results–Thefat client OS relies on the network shared resources,local exclusive resources,and semi-mobilehardware resources (OSPU), not relies solely on a single resource, to perform its tasks on a fat client, in thisarchitecture, most of the system files of OS on a fat client isderived from OS server, which is a network shared resources, and the rest of system files of OS is derived from OSPUof a fat client, which is a non-network resource, so the architecture of OShas "semi-network" attribute, wherein the OSPU is a key subordinate component for data processing and security verification,the OS server is a storage place rather than operating a placeof system files, and system files that stored on a server can only be downloaded to a fat client to carry out their mission.Conclusion–A complete OS is divided into base portion and expanded portion, and this "portion" division of OS enables a fat client to be dually supported by remote network resources and local non-network resources, therefore, it is expected to make a fat client more flexible, safer and more reliable, and more convenient to be operated.

NTAM-LSTM models of network traffic prediction

Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.

DEVELOPMENT OF A MATHEMATICAL MODEL FOR THE FUNCTIONING OF A MULTI-NODE DECENTRALIZED SYSTEM FOR MANAGING DATA FLOWS OF MOBILE DEVICES

В статье рассматривается модель распределения ресурсов сети, представленной множеством разнородных мобильных устройств, разнесенных в пространстве, обладающих возможностью динамического выхода из системы и перемещения в ней. Описана математическая модель и приведены результаты ее применения. The model of the distribution of network resources is discusses in the article. The network is represented by a multitude of heterogeneous mobile devices, spaced apart, with the ability to dynamically leaving the system and moving in it. A mathematical model is described and the results of its application are presented.

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced