6 private links
Radar-like technology can see through walls to track movement
Transport for London will roll out default wi-fi device tracking on the London Underground this summer, following a trial back in 2016. In a press release announcing the move, TfL writes that “secure, privacy-protected data collection will begin on July 8” — while touting addition…
With nothing but a smartphone and some clever computation, researchers can exploit ambient signals to track individuals in their own homes.
RF-Capture has many applications, like:
It can know who the person behind a wall is.
It can trace a person's handwriting in air from behind a wall.
It can determine how a person behind a wall is moving .
researchers of the NETMIT group at MIT’s Computer Science and Artificial Intelligence Lab, are using WiFi signals to detect the breathing and heart rate of individuals in a room. They’ve just released a couple videos showing off the technology in action
The URLs [1] [2] describe the content. I thought [1] was interesting but not answering your question. [2] Answers your question, and shows black and white and thermal pictures.
[1] https://www.medgadget.com/2014/06/mits-wifi-system-detects-p... (June 2014)
[2] https://hackaday.io/project/5452-wifi-thermal-camera (2015)
[EDIT] I stand corrected, [2] is unrelated. My bad! Here's some good sources as alternative.
"MIT turns Wi-Fi Into Indoor GPS New tech from CSAIL lab lets one Wi-Fi device locate another to within centimeters" [3]
"RF-Capture: Capturing the Human Figure Through a Wall
It can know who the person behind a wall is. It can trace a person's handwriting in air from behind a wall. It can determine how a person behind a wall is moving." [4]
They also contain further resources.
[3] https://spectrum.ieee.org/tech-talk/telecom/wireless/mit-tur...
Our Solutions
Technology Overview
Our contextual technology is based on one key principle: simplicity. We rely on existing wireless signals to recognize people (and animals too), gather data about what they are doing and where they are located. Based on this data, we can automate workflows and provide contextual information that can be
I recently listened to a podcast with the founders of a startup by the name of Aerial (https://aerial.ai) that that is doing real-time location mapping and activity detection using wifi and deep learning.
NOTE: I'm in no way related to this company or the podcast (aside from being an occasional listener).
Episode page: https://twimlai.com/talk/107
Direct episode link: https://feeds.soundcloud.com/stream/393602724-twiml-twiml-ta...
Episode description:
In this episode I’m joined by Michel Allegue and Negar Ghourchian of Aerial.ai. Aerial is doing some really interesting things in the home automation space, by using wifi signal statistics to identify and understand what’s happening in our homes and office environments.
Michel, the CTO, describes some of the capabilities of their platform, including its ability to detect not only people and pets within the home, but surprising characteristics like breathing rates and patterns. He also gives us a look into the data collection process, including the types of data needed, how they obtain it, and how it is parsed. Negar, a senior data scientist with Aerial, describes the types of models used, including semi-supervised, unsupervised and signal processing based models, and how they’ve scaled their platform, and provides us with some real-world use cases.
Décryptage de la menace qui pèse sur le Wi-Fi
KRACK attack allows other nasties, including connection hijacking and malicious injection.
The big news in crypto today is the KRACK attack on WPA2 protected WiFi networks. logo-smallDiscovered by Mathy Vanhoef and Frank Piessens at KU Leuven, KRACK (Key Reinstallation Attack) leverages a vulnerability in the 802.11i four-way handshake in order to facilitate decryption and forgery attacks on encrypted WiFi traffic.
Breaking WPA2 by forcing nonce reuse
Le groupe de pirates, APT28, utilise la faille de WannaCry pour pirater le WiFi de plusieurs hôtels en Europe et voler des données clients.