Traffic Patterns Research Articles

DETECTION OF WIRELESS NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE

The security and integrity of computer networks are seriously threatened by network assaults. Keeping a safe network environment requires the capacity to anticipate and stop these threats. Supervised machine learning methods have become powerful instruments for attacking network traffic and spotting patterns that point to malicious behaviour. We provide an in-depth examination of supervised machine learning methods for network attack prediction. We gather the data, preprocess it, extract pertinent features, and structure it so that machine learning algorithms may use it.We assess these algorithms' performance. To understand the fundamental patterns and traits of network assaults, we look at how interpretability the trained models are. This enables network managers to comprehend the types of threats and create suitable. Keywords - Network Attacks, Supervised Machine Learning, Network security,Network traffic analysis, Malicious behavior detection, Feature extraction,Machine learning algorithm,Model interpretability,Network attack patterns,Network defense strategies,Naive Bayes Algorithm, Ridge Classfier ,Random Forest Classfier

Web analytics and performance metrics: assessing the effectiveness of digital strategies

Web analytics involves applying data analysis to the information collected by a website to understand traffic patterns and user behavior. The evaluation of web analytics in the tourism sector in Cuenca, Ecuador, sought to improve digital strategies through a mixed qualitative-quantitative approach. A correlation was observed between age and the frequency of consulting statistics on tourism websites, with greater participation of young people. In addition, a significant interest in receiving personalized recommendations was highlighted, with about 40 % expressing definite interest. The willingness to provide feedback and participate in the improvement of online services was widespread. The Internet was positioned as the main source of tourism information, with an emphasis on the use of social networks. These findings highlight the importance of digital marketing strategies and optimization for mobile devices in the tourism industry.

Real Time Accident Detection and Ambulance Rescue using Deep-Learning

Traffic accidents pose a significant threat to global safety. A comprehensive approach uses machine learning frameworks, deep learning techniques, and Python modules to reduce casualties and mitigate damage. The system uses Google API location services for precise geolocation tracking and improves accident prediction accuracy. It also enhances ambulance dispatching efficiency by dynamically assigning ambulances based on location and traffic patterns

Ant Mill: an adversarial traffic pattern for low-diameter direct networks

AbstractSince today’s HPC and data center systems can comprise hundreds of thousands of servers and beyond, it is crucial to equip them with a network that provides high performance. New topologies proposed to achieve such performance need to be evaluated under different traffic conditions, aiming to closely replicate real-world scenarios. While most optimizations should be guided by common traffic patterns, it is essential to ensure that no pathological traffic pattern can compromise the entire system. Determining synthetic adversarial traffic patterns for a network typically relies on a thorough understanding of its topology and routing. In this paper, we address the problem of identifying a generic adversarial traffic pattern for low-diameter direct interconnection networks. We first focus on Random Regular Graphs (RRGs), which represent a typical case for these networks. Moreover, RRGs have been proposed as topologies for interconnection networks due to their superior scalability and expandability, among other advantages. We introduce Ant Mill, an adversarial traffic pattern for RRGs when using routes of minimal length. Secondly, we demonstrate that the Ant Mill traffic pattern is also adversarial in other low-diameter direct interconnection networks such as Slimfly, Dragonfly, and Projective networks. Ant Mill is thoroughly motivated and evaluated, enabling future studies of low-diameter direct interconnection networks to leverage its findings.

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic.

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

Dynamic stress analysis of rock joints under railway loading

The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.

Mobile User Traffic Generation via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

In the increasingly complex urban transportation landscape, the necessity for accurate, multi-step forecasting has never been more apparent to help plan for long-term, strategic transportation initiatives like Intelligent Transportation Systems. This work focuses on resolving uncertainties around the proper training step size for machine learning models performing multi-step traffic flow forecasting. Two strategies are considered: one-step and horizon-step training sizes, which extend from the single-step forecasting method. This paper compares and evaluates two machine learning models: the Support Vector Regression and Long Short-Term Memory (LSTM). Data from two country road locations, including urban roads in Kuala Lumpur and the I5-North freeway in California, are employed to forecast traffic flow in 1-hour increments, projecting from 2 h up to 24 h ahead. The results reveal a significant difference in performance between the two training step sizes, with the one-step training size emerging as the more consistent and optimal strategy for both Direct and Multi-Input Multi-Output forecasting strategies. The proposed model, the Direct Particle Swarm Optimization Grid Search Support Vector Regression (Direct-PSOGS-SVR) model, exhibited comparable or slightly better performance than the LSTM model in both environments. In the I5-North freeway dataset, the Direct-PSOGS-SVR achieved similar Root Mean Squared Error values across most forecasting tasks compared to the LSTM, indicating its effectiveness in stable traffic flow patterns. Notably, in the more dynamic urban environment of Kuala Lumpur, the Direct-PSOGS-SVR model also demonstrated stability and resilience in forecasting accuracy, effectively handling the inherent noise and fluctuations in traffic patterns. These findings serve as a valuable guide for practitioners in selecting the most efficacious combination of training strategies for specific time series forecasting tasks utilizing machine learning models, particularly in traffic flow forecasting. Introducing the Direct-PSOGS-SVR model enriches the landscape of machine learning solutions for traffic forecasting, underscoring the paper’s contributions to the broader understanding of time series forecasting dynamics.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

ROBUST LEARNING AND ACCURATE PREDICTION OF WIRELESS TRAFFIC USING IMPROVED NEURAL NETWORK

The proposed system introduces a novel Deep Metric Algorithm as a cornerstone for enhancing the feasibility and optimality of network traffic prediction within the dynamic landscape of cloud technologies. The Deep Metric Algorithm is designed to operate as a physically interpretable probabilistic model that captures network-wide traffic patterns. Unlike traditional methods that rely solely on current traffic states observed at specific intervals, this algorithm leverages an initially expensive set of measurements to construct a network-specific traffic model.The algorithm's innovation lies in its ability to utilize this network-specific model alongside readily available, less expensive measurements, thereby significantly improving the accuracy of traffic predictions. By employing the Deep Metric Algorithm, the system can predict traffic fluctuations not only over observed links but also extend its predictions to unobserved links. This is a crucial advancement as it enables the system to offer more comprehensive insights into network behavior, even where direct measurements might be limited.One notable strength of the proposed Deep Metric Algorithm is its demonstrated applicability across various traffic periods within the same network. This adaptability ensures that the learned model remains effective over time, showcasing its potential utility in optimizing network performance as it evolves. The algorithm's predictive capabilities, coupled with its ability to adapt to changing network conditions, position it as a valuable tool for load-aware resource management and predictive control, thereby contributing to the efficient management of multi-provider networks in the evolving cloud landscape. KEYWORDS: Quality of Service (QoS), network traffic, accurate predictions, network-specific traffic model, Deep Metric Algorithm.

Optimizing Resource Allocation in Cloud Computing Using Machine Learning

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid computing and cloud computing. In grid computing, many geographically dispersed clusters are linked and made available to the general public. Materials and Methods: We looked at Monte Carlo Tree Search and Long-Short Term Memory and examined how efficient they were. By maintaining consistent traffic patterns, the simulation demonstrated that MCTS performance was improved. However, such a strategy is difficult to implement due to the potential speed with which traffic patterns may alter. A successful service level agreement (SLA) was achieved, and the issue was shown to be fixable using LSTM. We compare the suggested model to different load-balancing algorithms to find the one that best allocates resources. Findings: The results show that compared to the state-of-the-art models, the suggested model achieves an accuracy rate that is 10-15% higher. The suggested model lowers the error percentage rate of the average request blocking likelihood of traffic load by around 9.5-10.2% when compared to the predictions of existing models. Therefore, the proposed method has the potential to enhance network utilization by reducing the amount of time required by memory and the central processing unit. Implications to Theory, Practice and Policy: One advantage of the new method is a more robust forecasting strategy in comparison to earlier models. Using firefly algorithms, future research will construct a cloud data center that employs a variety of heuristics and machine learning methodologies to load balance the energy cloud (Oshawa et al., 2022).

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Darknet Traffic Analysis Investigating the Impact of Modified Tor Traffic on Onion Service Traffic Classification

Darknet Traffic Analysis Investigating the Impact of Modified Tor Traffic on Onion Service Traffic Classification

Learned load balancing

Effectively balancing traffic in datacenter networks is a crucial operational goal. Most existing load balancing approaches are handcrafted to the structure of the network and/or network workloads. Thus, new load balancing strategies are required if the underlying network conditions change, e.g., due to hard or grey failures, network topology evolution, or workload shifts. While we can theoretically derive the optimal load balancing strategy by solving an optimization problem given certain traffic and topology conditions, these problems take too much time to solve and makes the derived solution stale to deploy. In this paper, we describe a load balancing scheme Learned Load Balancing (LLB), which is a general approach to finding an optimal load balancing strategy for a given network topology and workload, and is fast enough in practice to deploy the inferred strategies. LLB uses deep supervised learning techniques to learn how to handle different traffic patterns and topology changes, and adapts to any failures in the underlying network. LLB leverages emerging trends in network telemetry, programmable switching, and “smart” NICs. Our experiments show that LLB performs well under failures and can be expanded to more complex, multi-layered network topologies. We also prototype neural network inference on smartNICs to demonstrate the workability of LLB.

Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.

Detection of Botnet in IOT Using Machine Learning

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

Unravelling the last-mile conundrum: A comparative study of autonomous delivery robots, delivery bicycles, and light commercial vehicles in 14 varied European landscapes

In this study, the growing need for efficient delivery services in the expanding e-commerce sector is addressed, with a focus on real-life consumption data. A comprehensive modelling framework is proposed to evaluate the efficiency of various transportation modes, including Light Commercial Vehicles (LCVs), cargo bicycles, and Autonomous Delivery Robots (ADRs). Utilizing the Google API, delivery destinations are identified, origin-destination matrices are created, and routes are optimized using Google OR-Tools and a capacitated vehicle routing problem solver. The study's robustness is further enhanced by incorporating real-life consumption data, considering diverse European contexts, varying urban scales, traffic patterns, and topographical factors, thus assessing their impact on transportation efficiency. The findings reveal that ADRs are efficient in pedestrian-focused, traffic-limited areas, while bicycles are more effective in dense city centres. This research highlights the necessity of tailoring transportation mode choice to specific urban characteristics for optimal efficiency and consumer satisfaction.Overall, the present study offers valuable insights into optimizing delivery services in different urban settings, providing a significant model for improving last-mile delivery systems. It contributes to understanding how different transportation modes can be effectively integrated into urban logistics, addressing environmental sustainability, operational efficiency, and real-life consumer demands.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Machine learning for network defense: automated DDoS detection with telegram notification integration

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.