WiserAnalyzer: A Passive Monitoring Framework for WLANs

Chhetri, Arun; Zheng, Rong

doi:10.1109/msn.2009.39

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Kismet only provides basic analysis of traffic (enumerating networks, hosts and amount of data), higher level analysis is not available. In addition to this, Kismet [12] Benmoshe et al [2] Chhetri et al [6] Yang et al [5] Chen et al [4] Kismet does not have a detailed visualization tool. Some tools have been built on top of Kismet, mainly for the purpose of visualizing the node locations (using GPS plugins) but several of them are no longer maintained and use outdated libraries.…”

Section: Related Workmentioning

confidence: 99%

IoTScanner

Siby

Maiti

Tippenhauer

2017

Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security

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In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments.In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we investigate metrics that could be used to classify devices and identify privacy threats in an IoT neighborhood.

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Section: Related Workmentioning

confidence: 99%

IoTScanner

Siby

Maiti

Tippenhauer

2017

Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security

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“…Even with pure strategies, the optimization problem defined in (5) is still challenging to solve directly. The main difficulty arises from the evaluation of E [w(x k )].…”

Section: Qom Under the Sniffer-centric Modelmentioning

confidence: 99%

“…We vary the total number of orthogonal channels from 3 to 9. 5 The results shown are the average of 20 runs with different seeds.…”

Section: Simulation Validationmentioning

confidence: 99%

“…These devices capture transmissions of wireless devices or activities of interference sources in their vicinity and store the information in trace files, which can be analyzed distributively or at a central location. Wireless monitoring [1], [2], [3], [4], [5] has been shown to comple-ment wire side monitoring using SNMP and basestation logs since it reveals detailed PHY (e.g., signal strength, spectrum density) and MAC behaviors (e.g, collision, retransmissions), as well as timing information (e.g., backoff time), which are often essential for wireless diagnosis. The architecture of a canonical monitoring system consists of three components: 1) sniffer hardware, 2) sniffer coordination and data collection, and 3) data processing and mining.…”

Section: Introductionmentioning

confidence: 99%

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On Quality of Monitoring for Multi-channel Wireless Infrastructure Networks

Nguyen¹,

Scalosub²,

Zheng³

2013

Preprint

Self Cite

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Passive monitoring utilizing distributed wireless sniffers is an effective technique to monitor activities in wireless infrastructure networks for fault diagnosis, resource management and critical path analysis. In this paper, we introduce a quality of monitoring (QoM) metric defined by the expected number of active users monitored, and investigate the problem of maximizing QoM by judiciously assigning sniffers to channels based on the knowledge of user activities in a multi-channel wireless network. Two types of capture models are considered. The user-centric model assumes frame-level capturing capability of sniffers such that the activities of different users can be distinguished while the sniffer-centric model only utilizes the binary channel information (active or not) at a sniffer. For the user-centric model, we show that the implied optimization problem is NP-hard, but a constant approximation ratio can be attained via polynomial complexity algorithms. For the sniffer-centric model, we devise stochastic inference schemes to transform the problem into the user-centric domain, where we are able to apply our polynomial approximation algorithms. The effectiveness of our proposed schemes and algorithms is further evaluated using both synthetic data as well as real-world traces from an operational WLAN.

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“…While much work has been done in the area of passive monitoring of WLAN networks [1], [2], [3], [4], [5], monitoring wide-band spectrum usage is fundamentally more difficult for a number of reasons. First, the spectrum range of interests is generally broader than the WiFi bands.…”

Section: Introductionmentioning

confidence: 99%

Binary Blind Identification of Wireless Transmission Technologies for Wide-Band Spectrum Monitoring

Nguyen

Zheng

et al. 2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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Spectrum monitoring is important to ensure the safe operation of mission critical systems as well as the satisfactory performance of non-critical applications over wireless. In this paper, we present a novel blind technology identification (BTI) approach that utilizes only binary representation of spectrum activities to identify transmission technologies that are present in the radio spectrum. Spectrum observations are modeled as Boolean OR mixtures on the underlying signal sources, and the binary independent component analysis technique is applied. Not only can we reveal the latent independent wireless technologies, but their transmission statistics and activities at each time slot can also be inferred. Evaluation results on both synthetic and real spectrum traces show that without any high-level features, the proposed methodology achieves high inference accuracy in noisy measurements.

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WiserAnalyzer: A Passive Monitoring Framework for WLANs

Cited by 15 publications

References 18 publications

IoTScanner

IoTScanner

On Quality of Monitoring for Multi-channel Wireless Infrastructure Networks

Binary Blind Identification of Wireless Transmission Technologies for Wide-Band Spectrum Monitoring

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