Cloning Mac Address Results Review

Every network interface has a media access control (MAC) address. Network interface cards come from the factory with a unique MAC address associated with the hardware. Most network cards and routers allow one to set a custom MAC address [1], overriding the MAC address present in the hardware. Cloning a MAC address, or changing the MAC address on one device to the MAC address associated with a different device, can be useful when an Internet connection is associated with a particular MAC address and that MAC address is no longer existed in the network. On the other hand if the ISP blocked the Mac address of the original device, changing Mac address is efficient way to communicate to the internet. Another function of cloning MAC address is used to jam the network with the IP address conflict associated with two devices.  

Location-based Data Analysis of Visitor Structure for Recreational Area Management

This work presents a location-based data analysis framework for profiling visitors structures. In terms of recreational area management, understanding visitors’ structure and popularity is important. Traditionally, visitors monitoring with automatic counting devices has drawbacks of inaccurate visitors counting. In this work, compared to automatic counting devices, we use Wi-Fi tracking as the main method to count visitors, which provides a fairly precise picture of visitor structures. Moreover, we deliver rich analytic functions in this framework and we present the functionality with visitor data collected from Guanyinshan Visitor Center. This framework not only standardizes visitor counting process but also facilitates a profound analysis of visitor structures. Key Words: Guanyinshan Visitor Center, Wi-Fi trackin

Выборочное обследование пассажиропотока методом анализа Wi-Fi данных в московском транспортном узле. Часть 1

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks.For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant.This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly.The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool.The study has revealed that, on average, up to 40% of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices.The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. You can find the first part of the article in the issue.В современных, быстро развивающихся городах мира для построения транспортной модели городов требуются данные о пассажиропотоках. Отсутствие таких данных не позволяет своевременно принимать управленческие решения на уровне их распределения, в том числе в рамках общих транспортных потоков.На данный момент существуют различные методы и системы для подсчёта пассажиропотоков, такие как глазомерный, анкетный и талонный методы и различные автоматизированные системы. Однако известные методы имеют свои недостатки.По этой причине актуальной является задача поиска альтернативных методов и источников данных для исследования пассажиропотоков. Данная статья опирается на актуализированные результаты исследования, проведённого в рамках подготовки автором магистерской диссертации. В его процессе и в развитие более ранних работ автора, в качестве источника данных были выбраны данные о подключении пассажиров к Wi-Fi роутерам. Так как на данном этапе исследование приводилось на территории Московского транспортного узла, в метрополитене и на Московских центральных диаметрах (МЦД), в вагонах которых установлено огромное количество Wi-Fi роутеров, при подключении к которым можно бесплатно получить доступ в Интернет, это значительно расширило выборку Wi-Fi данных.Целью данного исследования является изучение возможностей обработки Wi-Fi данных, полученных от Wi-Fi сканеров, в качестве инструмента анализа пассажиропотоков.По результатам проведённого исследования было определено, что в среднем до 40 % пассажиров, находящихся в вагонах метрополитена и МЦД на исследовавших линиях поездок, используют включённый Wi-Fi модуль в своём мобильном устройстве.Результаты исследования подтвердили, что Wi-Fi данные могут быть использованы в качестве инструмента для анализа пассажиропотоков, но в то же время выявили необходимость сочетать их с другими источниками данных, а также сильную зависимость результатов обработки Wi-Fi от технических характеристик Wi-Fi сканера и его расположения в транспортном средстве при проведении замеров.В данном номере публикуется первая часть статьи

Practical Hash-based Anonymity for MAC Addresses

Given that a MAC address can uniquely identify a person or a vehicle, continuous tracking over a large geographical scale has raised serious privacy concerns amongst governments and the general public. Prior work has demonstrated that simple hash-based approaches to anonymization can be easily inverted due to the small search space of MAC addresses. In particular, it is possible to represent the entire allocated MAC address space in 39 bits and that frequency-based attacks allow for 50% of MAC addresses to be enumerated in 31 bits. We present a practical approach to MAC address anonymization using both computationally expensive hash functions and truncating the resulting hashes to allow for k-anonymity. We provide an expression for computing the percentage of expected collisions, demonstrating that for digests of 24 bits it is possible to store up to 168,617 MAC addresses with the rate of collisions less than 1%. We experimentally demonstrate that a rate of collision of 1% or less can be achieved by storing data sets of 100 MAC addresses in 13 bits, 1,000 MAC addresses in 17 bits and 10,000 MAC addresses in 20 bits.Comment: Accepted at the 17th International Conference on Security and Cryptography (SECRYPT 2020). To be presented between 8-10 July 202

Wombat: An experimental Wi-Fi tracking system

National audienceIn this paper, we present Wombat, a Wi-Fi tracking platform aiming at improving user awareness toward physical tracking technologies and at experimenting new privacy-preserving mechanisms. Elements of this system are presented along with its architecture. We also present the use of Wombat in the context of a demonstration scenario. We introduce a new privacy-enhancing feature developed on top of Wombat: a Wi-Fi-based opt-out mechanism that allows users to easily express their opt-out decision

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs