Abrupt ending of 802.11 AP connections

Massa, Dossa; Morla, Ricardo

doi:10.1109/iscc.2013.6754971

Cited by 5 publications

(5 citation statements)

References 13 publications

(7 reference statements)

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“…In [22], a usage pattern called "abrupt ending" is explored in a similar dataset, and it concerns the disassociation of a large number of wireless sessions in the same AP within a one second window, or in a nutshell "simultaneous session ending". The authors of this work, further investigate this concept and introduce some anomalous patterns that might be in correlation with the occurrence of this phenomena.…”

Section: Usage Modeling and Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection and Modeling in 802.11 Wireless Networks

Allahdadi

Morla

2018

J Netw Syst Manage

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IEEE 802.11 Wireless Networks are getting more and more popular at university campuses, enterprises, shopping centers, airports and in so many other public places, providing Internet access to a large crowd openly and quickly. The wireless users are also getting more dependent on WiFi technology and therefore demanding more reliability and higher performance for this vital technology. However, due to unstable radio conditions, faulty equipment, and dynamic user behavior among other reasons, there are always unpredictable performance problems in a wireless covered area. Detection and prediction of such problems is of great significance to network managers if they are to alleviate the connectivity issues of the mobile users and provide a higher quality wireless service. This paper aims to improve the management of the 802.11 wireless networks by characterizing and modeling wireless usage patterns in a set of anomalous scenarios that can occur in such networks. We apply time-invariant (Gaussian Mixture Models) and time-variant (Hidden Markov Models) modeling approaches to a dataset generated from a large production network and describe how we use these models for anomaly detection. We then generate several common anomalies on a Testbed network and evaluate the proposed anomaly detection methodologies in a controlled environment. The experimental results of the Testbed show that HMM outperforms GMM and yields a higher anomaly detection ratio and a lower false alarm rate.

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Section: Usage Modeling and Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection and Modeling in 802.11 Wireless Networks

Allahdadi

Morla

2018

J Netw Syst Manage

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“…In Massa and Morla, 13 a usage pattern called “abrupt ending” is explored in a FEUP dataset 6,7 that concerns the disassociation of a large number of wireless sessions in the same AP within a 1 second window. The authors introduced some anomalous patterns that might be in correlation with the occurrence of this phenomenon, for instance, AP halt/crash, AP overload, persistence interference, and intermittent connectivity.…”

Section: Related Workmentioning

confidence: 99%

802.11 wireless simulation and anomaly detection using HMM and UBM

2020

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Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

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“…In [13], a usage pattern called "abrupt ending" is explored in FEUP dataset [6,7] that concerns the disassociation of a large number of wireless sessions in the same AP within a one second window.…”

Section: Anomalous Pattern Detectionmentioning

confidence: 99%

802.11 Wireless Simulation and Anomaly Detection using HMM and UBM

Allahdadi¹,

Morla²,

Cardoso³

2017

Preprint

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Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. Complication of monitoring the broaden and complex WLANs, that often requires heavy instrumentation of the user devices, makes the anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches (RawData and PCA). The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

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Abrupt ending of 802.11 AP connections

Cited by 5 publications

References 13 publications

Anomaly Detection and Modeling in 802.11 Wireless Networks

Anomaly Detection and Modeling in 802.11 Wireless Networks

802.11 wireless simulation and anomaly detection using HMM and UBM

802.11 Wireless Simulation and Anomaly Detection using HMM and UBM

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