Traffic Generation Research Articles

Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.

Traffic trajectory generation via conditional Generative Adversarial Networks for transportation Metaverse

The transportation Metaverse, by integrating real and virtual vehicular networks, brings significant benefits to the development of smart cities. However, the difficulty and high cost of conducting large-scale traffic and driving simulations in the transportation Metaverse via realistic data collection and fusion from the physical world directly result in the lack of spatial–temporal traffic data and privacy concerns, which significantly hinder the development of the transportation Metaverse. Hence, in these situations, it becomes essential to produce high-quality, large-scale trajectory data to support relevant applications. However, the deficiency of data-driven method is the heavy dependence on high-quality historical data. Prior knowledge can help reduce learning difficulties and overfitting problems with small amounts of data. In our study, we use a hybrid framework, the Travel Demand Conditioning Generative Adversarial Network (TD-GAN), which combines data-driven and knowledge-driven approaches to address the issue of traffic trajectory generation. First, we employ a conditional mechanism to incorporate prior knowledge of travel demand to reduce learning difficulties. Second, non-standard convolutional module and multi-headed self-attention module are developed to capture spatial–temporal correlations. The experimental results show that our model outperforms the baseline models.

Automatic Traffic E-challan Generation

Utilizing state-of-the-art technology, Automated Traffic E-challan Generation systems are employed to promptly detect and document traffic violations as they occur. By seamlessly integrating cameras, sensors, and sophisticated algorithms, these systems independently identify transgressions including but not limited to speeding and unauthorized parking. After detection, electronic citations (e-challans) are swiftly produced and delivered to vehicle proprietors, thereby optimizing enforcement procedures and bolstering overall road safety. However, notwithstanding their efficacy, the deployment of such systems mandates the meticulous consideration of privacy and equity issues, necessitating the enactment of suitable regulatory protocols. The Automatic recognition of license plate is the basis of effective management in traffic, the automatic detection and localization of license plate is an important part. Keywords- Python, Raspberry Pi, Convolutional Neural Network

Postdigital theatre: Beyond digital alienation of live theatre on the Nigerian performance space

The challenges of studies in humanities in the wake of the ongoing digital revolution in science and technology sum up the crisis of the place of live theatre in response to skit making on social media apart from the traditional film medium like cinemas and home videos. There are perhaps arguments around the presumed effectiveness of digital theatre in terms of its ability to reach wider audiences across the globe at once through the deployment of technology. Nevertheless, it is clear from my experience of live theatre as recent as May 15 and June 19 2022 by the House of Arimata Theatre Studio in Osogbo, that digital theatre cannot take the place of a live theatre. The communal ritual essence of live theatre, its liminal phases, the necessary separation, the shattering of the performers in the limen, their re-assemblage/merging with the audience, and the communal re-emergence with a new consciousness, all in the transitions and the festivity of a live theatre, cannot be captured in/by a digital theatre. Thus, the lacuna is clear even in the rate at which humanity is commoditised with its knowledge and cultural productions monetised in most digital theatre productions. It is on the basis of the argument of postdigitalism that this paper uses direct observation and historical-analytic methods to argue that while live theatre retains the power to humanise through communal participation, digital theatre can only continue to commoditise/zombiefy humanity with its focus on traffic generation and advert placements essentially for materialism.

Research on the Simulation Method of HTTP Traffic Based on GAN

Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced a network traffic data normalization method based on Gaussian mixture models (GMM), and for the first time, incorporated a generator based on the Swin Transformer structure into the field of network traffic generation. To further enhance the robustness of the model, we mapped real data through an AE (autoencoder) module and optimized the training results in the form of evolutionary algorithms. We validated the training results on four different datasets and introduced four additional models for comparative experiments in the experimental evaluation section. Our proposed SEGAN outperformed other state-of-the-art network traffic emulation methods.

Air Travel Itinerary Market Share Prediction using Artificial Intelligence

The widely used Quality Service Index (QSI) model estimates air traffic by considering attributes such as capacity, connectivity, and travel time, which impact an itinerary market share (MS) in the origin-destination (OD) market. To determine itinerary attractiveness, the conventional QSI model combines these features, weighting them based on their significance and generates a score for each option. The key is therefore to select the optimal combination of weighted attributes that are able to forecast the MS with the desired accuracy. However, traditional QSI models rely on expert knowledge and assume that the traffic generation and allocation is always based in the same market principles, hindering their ability to generalize and identify new features. To address this, the present work, which is part of a research project entitled ERA funded by Red.es, introduces a generalized rationale based on Artificial intelligence using historical data, a challenge that has been faced in the early stages of the project. This approach enables the identification and understanding of key features in the aviation traffic and the development of a forecasting model capable of capturing more complex feature relationships.

Analysis of Software-Defined Networking (SDN) Performance in Wired and Wireless Networks Across Various Topologies, Including Single, Linear, and Tree Structures

The increasing prominence of the internet and the resulting heightened demand for flexibility and agility have rendered traditional networking solutions inadequate for meeting current computing needs. Software-Defined Networking (SDN) emerges as a solution to achieve these goals. A controller plays a crucial role in determining the success of SDN. Therefore, it is necessary to assess and compare the various SDN controllers used across different industries. In this study, we evaluate the effectiveness of two recognized SDN controllers, POX and Ryu. Our research employs the Mininet-Wi-Fi emulator, and we assess the aforementioned controllers using metrics such as Jitter, throughput, packet loss, and delay, utilizing the Distributed Internet Traffic Generator (D-ITG). What sets our research apart is its examination of network performance across both wired and wireless transmission modalities. Fast Ethernet was chosen as the speed for the wired medium, as it had not been studied before. Additionally, the packet size ranged from 128 to 1,024 bytes. We used single, linear, and tree topologies for comparison. Our experimental findings demonstrate that, in the majority of cases, Ryu offers significantly reduced latency, packet loss, and jitter compared to POX. Furthermore, the Ryu controller outperforms POX in terms of throughput, particularly in wireless networks.

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions.

Examination of Traditional Botnet Detection on IoT-Based Bots

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another.

Qualitative benefits of MBESST in the selection of a restaurant location: Touristic restaurant Palencia de Lara, Toledo, Spain

Selecting the optimal location for a restaurant is a process that demands crucial and comparable data. The Model for the Best Site Selection for a Restaurant (MBESST) is a flexible model that combines quantitative and qualitative elements in assessing any restaurant site. This research provides the qualitative results derived from evaluating the location of an empirical case, Palencia de Lara Restaurant in Toledo, Spain, a tourist city. The design consisted of a non-experimental, transversal, and qualitative approach, using non-participant observation and interviews to gain a desirable appreciation of the issue. The global assessment's locations were 7.26, indicating the collection of essential components to maximize its appeal to guests. Palencia de Lara Restaurant should optimize its entire operational capacity more effectively. Flow pattern, visibility, and massive traffic generators were the three most essential factors in selecting this location. A significant contribution of the research is that using the definitions of the factors and sub-factors and the scale of specific quality levels allows for focusing on standard terms during the location assessment process. The designed instruments in the MBESST facilitate users to get an integral assessment of any locations of nine factors, fifty-one sub-factors, and individual quality levels of the sub-factors, enabling an in-depth analysis of each assessed element and comparing each other under the same parameters, gathering quantitative and qualitative data.

Synthetic Maritime Traffic Generation System for Performance Verification of Maritime Autonomous Surface Ships

With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS’s ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance.

Software Platform for the Comprehensive Testing of Transmission Protocols Developed in GNU Radio

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring.

A Traffic Merging and Generation Framework for Realistic Synthesis of Network Traffic

A Traffic Merging and Generation Framework for Realistic Synthesis of Network Traffic

Traffic model in heterogeneous networks based on experimental data

Almost all modern networks provide data transfer between different subscriber devices types using subnets operating in different standards. Therefore, they are heterogeneous. To obtain correct estimates of the load and throughput of heterogeneous networks, it is necessary to be able to simulate such networks traffic close to real. Therefore, the traffic model development in heterogeneous networks based on experimental data is relevant. To do this, the article conducted an experimental study of real traffic in the operator “New Technologies of the XXI Century” heterogeneous network, analyzed the traffic generation main models in wireless networks, mathematical models IPP, 4IPP, IDP and 2IRP were selected for generating traffic in heterogeneous networks and determined the necessary for them application settings. Subscribers are classified depending on the subscriber devices types, the traffic types they use, and the data transferred volume. Based on the experimental data obtained, subscribers are classified depending on the subscriber devices types, the traffic types they use, as well as the data volume they transmit. A list of the subscribers’ main classes has been generated, indicating the models used to generate their traffic, and their parameters have been determined. A traffic mathematical model in heterogeneous networks has been developed, the transmission simulation of real and generated according to the developed model traffic over a heterogeneous network has been carried out, on the basis of which the correctness of the developed model has been shown. The resulting model allows us to obtain correct estimates of heterogeneous networks main characteristics using the mathematical modeling method.

Freight Trip Generation in Switzerland

This work introduces research which aims to fill the gap of reliable freight traffic generation rates for Switzerland. The results are use-specific generation rates and simple statistical models for estimating expected freight traffic trips which can be used for transport and spatial planning. The study uses data from three sources: disaggregated data from a survey of firms, nationwide aggregated data, and ad-hoc traffic measurements. This paper centres on the establishment-based survey, which collects information on size, equipment, and volume of freight traffic generated by 250 establishments across Switzerland. The survey was conducted using a stratified random sampling method.

MODEL OF RISE OF MOVEMENT AT ST. PAUL CATHOLIC JUNIOR HIGH SCHOOL IN PALANGKA RAYA

Traffic jams that occur in junior high schools during school hours can be prevented if before determining thelocation of the school, the school or other responsible parties first estimate the traffic generation on land use byknowing the magnitude of the generation, the geometry of the road sections in the area can be planned. school.The aim of this research is to obtain a movement generation model at schools in Palangka Raya City, which it ishoped can be used to estimate the number of movements coming to the school in the future, so that it can beused to anticipate problems arising from travel generation. This research was conducted at St. Paul CatholicMiddle School, Palangka Raya. The research used is quantitative descriptive research using survey methods. Themovement generation model for St. Paul's Catholic Middle School, Palangka Raya, after analyzing the simplelinear regression equation, which is most representative is Y4 = -2380,417 + 4,250 of 0.203. Factors that have astrong influence on the generation of movement at St Paul's Catholic Middle School in Palangka Raya are thenumber of students (X4) and the number of teachers and school employees (X5).

Dynamic Power Consumption in Base Transceiver Station in Urban and Rural Setting in Awka, Nigeria using Multi-Attribute Decision-Making Technique

The goal of this paper was to lower base transceiver station (BTS) dynamic power consumption. A multi-attribute decision-making technique was implemented that reflects the stochastic nature of cellular networks and guarantees cost-effective traffic generation and improved BTS power consumption performance. Every related candidate or attribute that is taken into consideration to help reduce power consumption is given a utility function, and the candidate with the highest utility value is chosen to meet the target. The Utility function that the Technique selects is the weighted sum of the normalized attributes. In contrast to the works of other authors, which lowered consumption by 20%, the developed dynamic power consumption model cut consumption to 25.14 percent. The two BTS of interest were measured, and the results showed that Mgbakwu BTS has two Peak (Busy) Hour periods: in the morning, 9.55Erlangs from 7.30am to 8.30am, and in the evening, 10.7Erlangs from 8.30pm to 9.30pm. The BTS is located in a rural/residential area. The BTS measurement and analysis for the UNIZIK temporary site revealed that it is located in a commercial/business setting with a morning peak (busy) hour period of 19.1 Erlangs between 10 and 11 a.m. 14.6 Erlangs is the afternoon peak hour, which runs from 3.30 to 4.30 p.m. The traffic generated in the BTS under various load situations is reflected in the relevant Power Consumption for both BTS.

Influence of Speed Brakers on Traffic Generated Noise Levels

Abstract This paper aims to study the on-going problem of traffic generated noise pollution, focusing on the influence of speed breakers on specific areas of the noise spectrum. We studied different types of speed breakers around the metropolitan area of Cluj-Napoca, measuring a sound spectrum from 20 Hz to 20 kHz. Speed breakers have an extensive use all over the world as safety measures near parks, schools, residential areas, but the question to answer is whether they also contribute to the increase in noise pollution and to what measure this contribution is. The study considers the different types of speed breakers used in the metropolitan area and the degree of usage the speed breakers have.

Analysis of E-commerce Live Streaming and Traffic Generation

The Internet has become an integral part of our society. This paper focuses on TikTok, one of today's most popular Internet platforms, and analyzes its e-commerce live streaming structure and traffic generation methods. Through a comprehensive examination of TikTok's current status and prospects, this study aims to deepen our understanding of the current Internet model and identify emerging trends. TikTok's commercial liquidity is essential to its success as a commercial platform. However, balancing commercial interests with user experience has challenged the platform's development. The key to TikTok's growth is creating a sticky user base and fostering a unique platform culture. Rather than prioritizing commercial gain over user experience, TikTok aims to engage users in the commercial process, which is critical for sustainable development. Overall, this paper sheds light on the complexities of e-commerce live streaming and traffic generation on TikTok and highlights the platform's unique approach to balancing commercial interests with user experience.

Economic contribution to the debate on cost sharing policy

This policy paper aims to contribute to understanding the recent debate on fair cost sharing between Big Techs and European telecommunication operators who offer complementary services: content and access to the Internet. We take stock from recent economic literature and empirical evidences to show the differences in business models and regulatory constraints have led to unequal net benefits from growing European digital economy. In particular, because the telecommunication operators have to bear the costs of delivering content to end-users, their business can be strongly affected due to the sharp increase in traffic generation. This negative externality should be internalized to correct the related market failure and to restore incentives to invest in the network infrastructure to meet the ambitious Europe's Digital Decade targets. Furthermore, we assess the interplay between access and content, and demonstrate how a fair cost sharing policy can impact investment, consumer surplus and social welfare. In particular, content providers that are efficient in monetizing consumers are incentivized to lower the cost of content to consumers, which positively impacts the consumer surplus and social welfare. Also, because cost sharing can even encourage digital platforms to better optimize the demand, this could have potentially positive effect on the environment. Finally, we give some recommendations to effectively implement the policy in practice.