Search Results for « Internet traffic patterns »

인터넷 트래픽의 성격은 최근에 극적으로 변화했으며 특히 인터넷 트래픽에서 동영상 트래픽이 차지하는 비중이 크게 증가했다. 동영상 트래픽은 트래픽의 패턴을 전통적인 쌍방향 통신에서 일방향 콘텐츠 전송으로 변화시키기 때문에 현행 정액제 중심의 인터넷망 요금구조는 이를 수용하기에 적절하지 않을 수 있다. 이러한 문제가 발생하는 가장 큰 이유는 트래픽(비용발생의 주요동인)과 수입간의 불연속이 있기 때문이다. 트래픽은 급격하게 성장할 것이라고 다 예측하고 있지만 트래픽을 전송하는 망 제공자의 수입은 그만큼 증가할 것이라 예측하기 힘들다. 본 논문에서는 예상되는 트래픽 성장을 감당하고 현행 수익모델을 개선하기 위한 다양한 대안들을 검토한다. 구체적으로 가용 네트워크 용량을 보다 효율적으로 사용하고 인터넷 서비스의 제공에 있어 보다 큰 혁신을 촉발시키기 위한 유인제도와 가격신호를 반영하는 대안을 평가기준에 따라 검토하고 바람직한 망이용대가 산정방안을 제시한다. The characteristics of internet traffic was dramatically changed recently. Especially, the ratio of video traffic in internet network is highly increased. Because the video traffic changes the traditional interactive traffic transport into one way contents delivery, internet network pricing structure is not efficient to reflect this changes. The fundamental reason for these is the discontinuity between the cost and revenue of the internet traffic. Although the internet traffic is forecasted to grow highly, the revenue of network provider is not expected to increase as such. The present paper investigates various alternatives to improve the present business model and to accommodate traffic growth. Then, the desirable options is proposed to invoke the innovation and to reflect the price signal according to some criteria.

The growing size and sophistication of the worldwide Internet systems require effective techniques of real-time monitoring of abnormal network activity.The given paper presents an active network probing framework which repeatedly measures the quality of end-to-end Internet paths with lightweight ICMP and TCP-based measurement packets.The framework is dynamically tweaked to either shorten or lengthen its probing interval, based on the volatility of the network that it monitors, allowing it to distinguish between a benign congestion event and an anomaly caused by an attack, e.g. by Distributed Denial-of-Service (DDoS) amplification or route hijacking.The probes are spread on ten geographically different vantage points that offer coverage and a high level of temporal granularity.Information regarding the collected data is analysed with a hybrid statistical-machine learning model that matches traffic deviations on real-time.Experimental tests had 92% accuracy in anomaly detection using less than 1% bandwidth footprint, which means that it does not cause much interference to the traffic being carried out.Findings prove the scalability, flexibility and efficiency of the framework in improving the visibility of the performance of the global Internet.The study will help in the development of non-invasive, proactive, and distributed network monitoring systems to ensure service reliability and safety in the current communication infrastructures.

Complex traffic patterns lead to crucial campus Metro-E network management and resource allocation. This paper presents an internet traffic forecasting by pre-processing data to offer better bandwidth quality of service (QoS). Eight (8) campuses' traffic data were analysed for modelling predictions using statistical analysis. A Metro-E campus network presents four (4) locations: A, E, F, and H have is a strong correlation between inbound and outbound traffic, with correlation values between 0.4547 and 0.5204. As the inbound traffic increases, outbound traffic tends to rise as well. Conversely, locations B, C, and G have weak correlations, indicating more independent traffic patterns. Data outliers were found for locations C and F, where unusual traffic spikes require further network exploration and show key trends in traffic data. Descriptive statistics reveal notable differences, with H has the highest average traffic at about 75 Mbps, while C has the lowest at around 30 Mbps. Location F shows the greatest traffic fluctuation with a standard deviation of 0.4076, whereas Location G has very little fluctuation with a standard deviation of 0.0240. Overall, this pre process data is use to combine machine learning (ML) to improve prediction abilities for better bandwidth management and real-time handling in digital campus environments.

We propose a new method of possible trend detection in the internet traffic by using sequential pattern analysis. The main steps of this method, such as the template pattern discovery in time-series data and pattern comparison and matching for sequential pattern analysis, are analyzed and the possible constraints and problems are pointed. Experimental data are depicted and future research subjects are described. Ill. 6, bibl. 9 (in English; summaries in English, Russian and Lithuanian).

We suggest a universal phenomenological description for the collective access patterns in the Internet traffic dynamics both at local and wide area network levels that takes into account erratic fluctuations imposed by cooperative user behaviour. Our description is based on the superstatistical approach and leads to the q -exponential inter-session time and session size distributions that are also in perfect agreement with empirical observations. The validity of the proposed description is confirmed explicitly by the analysis of complete 10-day traffic traces from the WIDE backbone link and from the local campus area network downlink from the Internet Service Provider. Remarkably, the same functional forms have been observed in the historic access patterns from single WWW servers. The suggested approach effectively accounts for the complex interplay of both “calm” and “bursty” user access patterns within a single-model setting. It also provides average sojourn time estimates with reasonable accuracy, as indicated by the queuing system performance simulation, this way largely overcoming the failure of Poisson modelling of the Internet traffic dynamics.

This study examines short-term Internet traffic patterns in Greece within the context of time-series dynamics and digital infrastructure behavior, aiming to identify temporal structures, daily cycles, and variability in network traffic. Using high-frequency data collected at 5-minute intervals over three months, this study adopts a multi-scale time-series analysis approach to investigate short-term Internet traffic dynamics. The analysis aggregates traffic data at hourly and daily levels and applies descriptive statistical analyses and visualization techniques, including hourly aggregation, daily averages, boxplots, and heatmaps. The findings reveal a strong and consistent diurnal pattern, with minimum traffic during the early morning hours (02:00–05:00) and peak demand in the evening (18:00–21:00). Inbound and outbound traffic exhibit near-perfect symmetry, indicating balanced network flows. Although mean traffic remains stable, significant short-term fluctuations and increased variability during peak periods are observed, highlighting both the stability and the dynamic nature of network behavior.

The rapid developments in mobile Internet are reshaping our lives and activities. Understanding the user behaviors and dynamics in such a large-scale network is essential for better system design, service provisioning, and network management. In this paper, we focus on the interaction pattern between mobile users and servers based on the traffic flow data. Real traffic flow data is collected from the public network of ISPs by high-performance network traffic monitors. Traffic flow-based heterogeneous information network (TF-HIN) is introduced to represent the traffic interaction pattern, and node correlation characteristics are mined from TF-HIN. Based on the empirical analysis of traffic interaction pattern, we propose the coupled flow tensor to represent the relations among the user, server and time, by incorporating correlations of user and server as auxiliary information. Two iterative algorithms, i.e., FTD and FTD-NFS, are proposed for coupled flow tensor decomposition and the latent factors are used for user clustering. We evaluate the proposed user clustering algorithms by using benchmark datasets and also analyze the user clustering results from real traffic flow dataset. The numerical experiments show that the use of coupled flow tensor with auxiliary information provides a novel and scalable user clustering method and improves the clustering accuracy.

The rapid development of communications together with the growing usage of Internet of Things have recently provided an opportunity for transformation of information and created a new generation of advertising, called smart advertising. The main aim of this research work is to develop a pattern on smart urban street advertising to offer a meaningful connection between the “parameters received from the environment” and the “planned messages” displayed by smart billboards. The main idea behind the proposed pattern is to use a billboard which is capable of sensing parameters through environmental, thermal, visual and magnetic sensors. In order to investigate the existing relations between the advertisement patterns, a questionnaire has been developed and used to collect audiences’ viewpoints for selecting ads according to the criteria sensed by smart billboard. Using a sample size of 107, collected data have been analyzed to evaluate the overall vision on how to select ads through measurable criteria. Research results revealed that there is a significant relation between environmental and traffic parameters and the advertising contexts suggested by smart billboard. Therefore proposing smart billboards by detecting the environmental and traffic parameters can improve ads performance to attract the audiences.

IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols' security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%.