Anomaly detection in university campus WiFi zones

Baras, Karolina; Moreira, Adriano

doi:10.1109/percomw.2010.5470669

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…Despite these limitations, prior studies have shown that Wi-Fi connections are as accurate as dedicated physical sensors (e.g., infrared beam-break or thermal sensors) for estimating student occupancy of university rooms and buildings 20,21 . The daily pattern of student Wi-Fi connections also conforms to expectations for different sites on campus including teaching spaces, libraries, food courts, and residential buildings 20,22–24 . An important limitation of our study is that we did not investigate student mixing with university staff or visitors because we only had Wi-Fi data for students.…”

Section: Mainsupporting

confidence: 55%

A targeted e-learning approach to reduce student mixing during a pandemic

Yeo

Lai

Tan

et al. 2020

Preprint

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11The COVID-19 pandemic has resulted in widespread closure of schools and universities. These institutions 12 have turned to distance learning to provide educational continuity. Schools now face the challenge of how to 13 reopen safely and resume in-class learning. However, there is little empirical evidence to guide decision-14 makers on how this can be achieved. Here, we show that selectively deploying e-learning for larger classes is 15 highly effective at decreasing campus-wide opportunities for student-to-student contact, while allowing most 16 in-class learning to continue uninterrupted. We conducted a natural experiment at a large university that 17 implemented a series of e-learning interventions during the COVID-19 outbreak. Analyses of >24 million 18 student connections to the university Wi-Fi network revealed that population size can be manipulated by e-19 learning in a targeted manner according to class size characteristics. Student mixing showed accelerated growth 20 with population size according to a power law distribution. Therefore, a small e-learning dependent decrease in 21 population size resulted in a large reduction in student clustering behaviour. Our results show that e-learning 22 interventions can decrease potential for disease transmission while minimizing disruption to university 23 operations. Universities should consider targeted e-learning a viable strategy for providing educational 24 continuity during early or late stages of a disease outbreak. 25 26 Main 27The coronavirus disease 2019 (COVID-19) has had enormous socioeconomic impact 1 . In 5 months, 5.5 28 million COVID-19 cases have been confirmed, resulting in 350,000 deaths across 188 countries 2 . The severe 29 acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 is primarily spread when an 30 infected person sneezes or coughs 3 . The spread of infection can be slowed by public health measures that 31 reduce person-to-person contact. Nonpharmaceutical interventions that include restricted travel, staying at 32 home, and physical distancing can delay and flatten the peak of COVID-19 cases to avoid the overwhelming of 33 medical services 4-6 . Nonpharmaceutical interventions therefore play a critical role in controlling the spread of 34 disease until effective vaccines or drugs are available 7 . 35 School closure is a key strategy for controlling the spread of infectious diseases 7-9 . Empirical and 36 modelling studies show that closing schools and universities can suppress COVID-19 transmission when 37 combined with other nonpharmaceutical interventions 5,6,10 . The COVID-19 pandemic has resulted in an 38 unprecedented number of school and university closures, affecting over 1.2 billion learners worldwide 11 . 39Consequently, a massive shift from classroom learning to distance learning has occurred 12 . This has created 40 great strain on educational institutions which function not only as places of learning, but also as major 41 employers and drivers of local economies. It is therefore important to con...

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Section: Mainsupporting

confidence: 55%

A targeted e-learning approach to reduce student mixing during a pandemic

Yeo

Lai

Tan

et al. 2020

Preprint

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“…There has been remarkable research on detection techniques for anomaly detection [1]. We will briefly discuss the approaches and the drawbacks of each of these techniques in this section.…”

Section: Related Workmentioning

confidence: 99%

Detection of side-channel communication in ad hoc networks using request to send (RTS) messages

Madtha

Martin

Liscano

et al. 2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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Wireless communication channels using the 802.11 protocol adopt the RTS/CTS mechanism to avoid collisions in the channel and to increase throughput. In a perfect scenario with no data loss, there exists a linear relationship between number of the valid frames received and the RTS received in the receiver. We propose a detection mechanism that keeps track of the rate of RTS per application data frames received where any deviation from its expected value could trigger an alarm for a possible sidechannel communication hidden by error frames. Several scenarios are simulated using QualNet simulator by varying the parameters to study the relationship between the good frames and the RTS. Based on the data collected we submit that this rate is a viable metric for detection.

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“…The importance of anomaly detection being in 802.11 networks or in any other application domain is that, usually anomalies in usage data translates to significant, and often critical, actionable decisions. Several anomaly detection techniques have been proposed in the literature ranging from rule based approach [13], classification based [14] and clustering based [15] approaches and information theoretical based approaches [16]. However these works fall short of addressing cases of abrupt ending of 802.11 AP connections and different anomaly-related patterns deduced besides these events.…”

Section: Related Workmentioning

confidence: 99%

Abrupt ending of 802.11 AP connections

Massa

Morla

2013

2013 IEEE Symposium on Computers and Communications (ISCC)

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Wireless 802.11 users often experience connectivity problems while using 802.11 networks. The task of diagnosing and fixing these problems by looking at usage patterns is one of the major challenges that campus and corporate 802.11 network administrators face. In this paper we identify a usage pattern that we name "abrupt ending" of 802.11 connections and that happens when a large number of sessions in the same access point (AP) end within a one second window. We observe up to 40 sessions ending at the same second and over 150k abrupt endings in a two and a half year period from 2006 to 2009 in the Faculty of Engineering of the University of Porto. We describe the data set and identify anomaly-related patterns such as AP halt/crash, AP overload, interference, interference across the vicinity of an AP, and AP persistent interference as well as user authentication failure and intermittent connectivity. We validate our analysis by density clustering of the abrupt ending data. In addition we crosscheck the existence of abrupt endings on a 2011 data set of the same location in Porto and on 2011 data from the University of Minho, which was deployed and is managed independently from the one in Porto.

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Anomaly detection in university campus WiFi zones

Cited by 14 publications

References 13 publications

A targeted e-learning approach to reduce student mixing during a pandemic

A targeted e-learning approach to reduce student mixing during a pandemic

Detection of side-channel communication in ad hoc networks using request to send (RTS) messages

Abrupt ending of 802.11 AP connections

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