Relevant Affect Factors of Smartphone Mobile Data Traffic

Husnjak, Siniša; Peraković, Dragan; Cvitić, Ivan

doi:10.7307/ptt.v28i4.2091

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…The development of public, packet-oriented, communication network (Internet) followed by the increasing number of users and IC services has resulted in an increase of the amount of transferred data [1,2]. Data that are stored, processed, and transmitted through the IC system are often the target of illegitimate users.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Distributed Denial of Service Traffic Detection Approaches

Cvitić¹,

Peraković

Periša

et al. 2019

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The availability of information and communication (IC) resources is a growing problem caused by the increase in the number of users, IC services, and the capacity constraints. IC resources need to be available to legitimate users at the required time. The availability is of crucial importance in IC environments such as smart city, autonomous vehicle, or critical infrastructure management systems. In the mentioned and similar environments the unavailability of resources can also have negative consequences on people's safety. The distributed denial of service (DDoS) attacks and traffic that such attacks generate, represent a growing problem in the last decade. Their goal is to disable access to the resources for legitimate users. This paper analyses the trends of such traffic which indicates the importance of its detection methods research. The paper also provides an overview of the currently used approaches used in detection system and model development. Based on the analysis of the previous research, the disadvantages of the used approaches have been identified which opens the space and gives the direction for future research. Besides the mentioned this paper highlights a DDoS traffic generated through Internet of things (IoT) devices as an evolving threat that needs to be taken into consideration in the future studies.

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Section: Introductionmentioning

confidence: 99%

An Overview of Distributed Denial of Service Traffic Detection Approaches

Cvitić¹,

Peraković

Periša

et al. 2019

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“…Users migrate from existing 2G networks to 3G and 4G connections to benefit from advanced mobile devices with greater functionality and higher data rates offered by these networks. LTE network users tend to almost double the data transmission compared to users of other mobile network data traffic generations [22]. LTE network has a higher data transfer rate than other mobile network generations.…”

Section: 70%mentioning

confidence: 99%

“…The amount of data traffic generated by a user who has access to a larger network generation with higher speeds significantly exceeds the amount of generated data of a user who had access to previous generation networks. This is proven because Android smartphones with the ability to access 4G networks have downloaded 13.1 GB of data traffic per month, while Android users with only 3G access can generate 5 GB of data traffic per user in the same month [22].…”

Section: 70%mentioning

confidence: 99%

Specifics of operating systems of terminal devices in generating data traffic

Husnjak¹,

Karlović²,

Kolarić³

2017

Proceedings of the 5th International Virtual Research Conference in Technical Disciplines

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This paper presents the differences between operating systems (OS) of terminal devices and their characteristics in generating data traffic. An important factor in generating data traffic is also the OS of terminal device because each OS does not generate equal amount of data traffic. The aim of this paper is to show how much data traffic generate terminal devices that use different OSs. By accessing different networks, the same device also detects differences in generating data traffic. Obtained data from the study also shows the differences and characteristics of each OS. Data traffic measurements were performed by viewing the same video track using the YouTube application on all terminal devices and using NetWorx, SurPlusMeter, My Data Manager and Daily Data Sense applications. Access to the data network was achieved using Wi-Fi, UMTS and LTE networks. Based on obtained data by this study, a comparison of data traffic was made for each OS.

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“…Husnjak et al [18] identified several factors affecting the generation of smartphone data traffic, including communication technology of the mobile network, size and resolution of the device monitor, device operating system, and price plan and options. Those authors then evaluated each factor using an adequate mathematical method, and those results were incorporated into the present work together with user preferences that Husnjak et al [19] determined to influence user smartphone data traffic offload from mobile to Wi-Fi networks; those preferences include data offload speed of the Wi-Fi network, availability of the Wi-Fi network, and price structure of access to that network.…”

Section: Identification Of Factors and Determination Of Preferencesmentioning

confidence: 99%

“…Questions on the survey, which were adapted from our previous work [18,19], took into account various contexts and ecosystems of smartphone use, as well as user behavioural patterns ( Table 1). The survey included questions such as "What is the size of the screen on your smartphone?," "How much data traffic is included in your mobile plan?," and "Do you have access to a Wi-Fi network at home?…”

Section: User Smartphone Usermentioning

confidence: 99%

Data Traffic Offload from Mobile to Wi‐Fi Networks: Behavioural Patterns of Smartphone Users

Husnjak

Peraković

Forenbacher

2018

Wireless Communications and Mobile Computing

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This paper presents a model for defining the behavioural patterns of smartphone users when offloading data from mobile to Wi-Fi networks. The model was generated through analysis of individual characteristics of 298 smartphone users, based on data collected via online survey as well as the amount of data offloaded from mobile to Wi-Fi networks as measured by an application integrated into the smartphone. Users were segmented into categories based on data volume offloaded from mobile to Wi-Fi networks, and numerous user characteristics were explored to develop a model capable of predicting the probability that a user with given characteristics will fall into a given category of data offloading. This model may prove useful for analysing smartphone user behaviour when offloading data.

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Relevant Affect Factors of Smartphone Mobile Data Traffic

Abstract: ABSTRACT

Cited by 6 publications

References 20 publications

An Overview of Distributed Denial of Service Traffic Detection Approaches

An Overview of Distributed Denial of Service Traffic Detection Approaches

Specifics of operating systems of terminal devices in generating data traffic

Data Traffic Offload from Mobile to Wi‐Fi Networks: Behavioural Patterns of Smartphone Users

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