15,394 research outputs found
Passive classification of Wi-Fi enabled devices
We propose a method for classifying Wi-Fi enabled mobile handheld devices (smartphones) and non-handheld devices (laptops) in a completely passive way, that is resorting neither to traffic probes on network edge devices nor to deep packet inspection techniques to read application layer information. Instead, classification is performed starting from probe requests Wi-Fi frames, which can be sniffed with inexpensive commercial hardware. We extract distinctive features from probe request frames (how many probe requests are transmitted by each device, how frequently, etc.) and take a machine learning approach, training four different classifiers to recognize the two types of devices. We compare the performance of the different classifiers and identify a solution based on a Random Decision Forest that correctly classify devices 95% of the times. The classification method is then used as a pre-processing stage to analyze network traffic traces from the wireless network of a university building, with interesting considerations on the way different types of devices uses the network (amount of data exchanged, duration of connections, etc.). The proposed methodology finds application in many scenarios related to Wi-Fi network management/optimization and Wi-Fi based services
Building up knowledge through passive WiFi probes
Inexpensive WiFi-capable hardware can be nowadays easily used to capture traffic from end users and extract knowledge. Such knowledge can be leveraged to support advanced services like user profiling, device classification. We review here the main building blocks to develop a system based on passive WiFi monitors, that is, cheap and viable sniffers which collect data from end devices even without an explicit association to any Wi-Fi network. We provide an overview of the services which can be enabled by such approach with three practical scenarios: user localization, user profiling and device classification. We evaluate the performance of each one of the three scenarios and highlight the challenges and threats for the aforementioned systems
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
Survey and Systematization of Secure Device Pairing
Secure Device Pairing (SDP) schemes have been developed to facilitate secure
communications among smart devices, both personal mobile devices and Internet
of Things (IoT) devices. Comparison and assessment of SDP schemes is
troublesome, because each scheme makes different assumptions about out-of-band
channels and adversary models, and are driven by their particular use-cases. A
conceptual model that facilitates meaningful comparison among SDP schemes is
missing. We provide such a model. In this article, we survey and analyze a wide
range of SDP schemes that are described in the literature, including a number
that have been adopted as standards. A system model and consistent terminology
for SDP schemes are built on the foundation of this survey, which are then used
to classify existing SDP schemes into a taxonomy that, for the first time,
enables their meaningful comparison and analysis.The existing SDP schemes are
analyzed using this model, revealing common systemic security weaknesses among
the surveyed SDP schemes that should become priority areas for future SDP
research, such as improving the integration of privacy requirements into the
design of SDP schemes. Our results allow SDP scheme designers to create schemes
that are more easily comparable with one another, and to assist the prevention
of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications
Surveys & Tutorials 2017 (Volume: PP, Issue: 99
Locational wireless and social media-based surveillance
The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date
Occupancy Estimation Using Low-Cost Wi-Fi Sniffers
Real-time measurements on the occupancy status of indoor and outdoor spaces
can be exploited in many scenarios (HVAC and lighting system control, building
energy optimization, allocation and reservation of spaces, etc.). Traditional
systems for occupancy estimation rely on environmental sensors (CO2,
temperature, humidity) or video cameras. In this paper, we depart from such
traditional approaches and propose a novel occupancy estimation system which is
based on the capture of Wi-Fi management packets from users' devices. The
system, implemented on a low-cost ESP8266 microcontroller, leverages a
supervised learning model to adapt to different spaces and transmits occupancy
information through the MQTT protocol to a web-based dashboard. Experimental
results demonstrate the validity of the proposed solution in four different
indoor university spaces.Comment: Submitted to Balkancom 201
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
With the rapid growth of the Internet-of-Things (IoT), concerns about the
security of IoT devices have become prominent. Several vendors are producing
IP-connected devices for home and small office networks that often suffer from
flawed security designs and implementations. They also tend to lack mechanisms
for firmware updates or patches that can help eliminate security
vulnerabilities. Securing networks where the presence of such vulnerable
devices is given, requires a brownfield approach: applying necessary protection
measures within the network so that potentially vulnerable devices can coexist
without endangering the security of other devices in the same network. In this
paper, we present IOT SENTINEL, a system capable of automatically identifying
the types of devices being connected to an IoT network and enabling enforcement
of rules for constraining the communications of vulnerable devices so as to
minimize damage resulting from their compromise. We show that IOT SENTINEL is
effective in identifying device types and has minimal performance overhead
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