Real Smart Home Data-Assisted Statistical Traffic Modeling for the Internet of Things

Majumdar, Chitradeep; López-Benítez, Miguel; Merchant, S. N.

doi:10.1109/jiot.2020.2969318

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…On demographic side, average house hold population, growth in population in cities and rural areas, shift of age group patterns in urban and rural population of the country were accounted for the estimations. On technology side, growth in internet enabled devices per house hold, S curves for adoption of new services and devices [21] and technical developments alluring individual users to upgrade were considered on the basis of data from different reporting agencies. The forecasting model applied for five different demographic patterns to predict the demand of an average user in a particular demography.…”

Section: Estimation and Forecasting Of Maximum Data Rate On Aggregati...mentioning

confidence: 99%

Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

Munir

Ali

Latif

2022

murjet

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Telecom service providers are on a relentless task of development and upgradation of their networks to support increasing requirements of internet users. FTTX landline connections serves multiple users connected on a single line hence need more robust and high data rate capabilities. In FTTX network, link capacity allocation for a service node to aggregating node requires statistical study of usage patterns of customers of the service areas. Different service areas have different usage patterns hence their statistical distributions are different. In this article we have acquired a yearlong data of peak hour utilizations from customers of the largest FTTX service provider in Pakistan to develop a statistical model. We developed an empirical distribution of peak hours of the customers mapped on day clock. This distribution has been used in Monte Carlo computation to find maximum data rate requirements on aggregation nodes in comparison to subscribed data rates of all users on a service node. Further a forecasting model has been used to predict the growth in subscriber demands in different demographic segments for coming years. A combination of maximum possible data rate requirement at aggregation node and forecasted subscriber data rates led to develop a forecast of data rate requirement for next five years in different demographic segments.

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Section: Estimation and Forecasting Of Maximum Data Rate On Aggregati...mentioning

confidence: 99%

Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

Munir

Ali

Latif

2022

murjet

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“…In a later paper [76] which extended the above-mentioned fingerprinting goal and method-3 https://iotanalytics.unsw.edu.au/iottraces ology, the results also highlighted the most prominent values of traffic features (e.g., distinctive destination ports) instead of comparing the number of unique values per hour between IoT and non-IoT (like we do). Regarding the variability in traffic predictability among IoT models (Subsection IV-B), the authors of [77] found statistically significant differences in the distribution of packet inter-arrival time among disparate IoT models, and the authors of [59] visualized the distribution of packet size among several IoT devices. However, each of these studies focused on one feature only, whereas we statistically compared among IoT models using four other (dynamic) features, while also correlating them with (static) device features.…”

Section: Association Of Iot Model Complexity With Network Traffic Pre...mentioning

confidence: 99%

“…In past research, e.g., [12], [78], [79], [84], these features were found informative for IoT attack detection. For instance, most normal IoT packets are sent at regular time intervals [77] (e.g., automated network activities). In contrast, these intervals are dramatically shorter [17] for most DoS attack traffic and thus informative.…”

Section: Nt: Networkmentioning

confidence: 99%

Responses to AHP-style questionnaires regarding the publication "D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks"

Benatar¹,

Biton²,

Shabtai³

2020

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IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel taxonomy for IoT attack detection which also encapsulates traffic predictability aspects, (c) propose an expertbased attack detectability estimation method which uses this taxonomy to derive a detectability score (termed 'D-Score') for a given combination of IoT model and attack scenario, and (d) empirically evaluate our method while comparing it with a datadriven method.

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IoT Security and Privacy

Tembhurne,

Diwan,

Jain

2023

Internet of Things

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Real Smart Home Data-Assisted Statistical Traffic Modeling for the Internet of Things

Cited by 12 publications

References 31 publications

Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

Responses to AHP-style questionnaires regarding the publication "D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks"

IoT Security and Privacy

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