Android is now the world’s largest earthquake detection network

Google leverages the massive scale of Android to do phone-based earthquake tracking.

Back in 2016, Ars reported on an interesting use for the bundle of sensors we carry around every day in our smartphones—earthquake detection. The accelerometers in your phone make a passable-enough seismometer, and together with location data and enough users, you could detect earthquakes and warn users as the shocks roll across the landscape. The University of California-Berkeley, along with funding from the state of California, built an app called "MyShake" and a cheap, effective earthquake detection network was born, at least, it was born for people who installed the app.

https://arstechnica.com/gadgets/2020/08/android-is-now-the-worlds-largest-earthquake-detection-network/

#Google #Android #Earthquake #detection #network

Visual Threat Intelligence

The most comprehensive database of detected deepfakes with actionable insights

👀 👉🏼 https://sensity.ai/

#deepfake #threat #detection #deeptrace
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag

Maltrail

Maltrail is a malicious traffic detection system, utilizing publicly available (black)lists containing malicious and/or generally suspicious trails, along with static trails compiled from various AV reports and custom user defined lists, where trail can be anything from domain name (e.g. zvpprsensinaix.com for Banjori malware), URL (e.g. hXXp://109.162.38.120/harsh02.exe for known malicious executable), IP address (e.g. 185.130.5.231 for known attacker) or HTTP User-Agent header value (e.g. sqlmap for automatic SQL injection and database takeover tool). Also, it uses (optional) advanced heuristic mechanisms that can help in discovery of unknown threats (e.g. new malware).

💡Architecture

Maltrail is based on the Traffic -> Sensor <-> Server <-> Client architecture. Sensor(s) is a standalone component running on the monitoring node (e.g. Linux platform connected passively to the SPAN/mirroring port or transparently inline on a Linux bridge) or at the standalone machine (e.g. Honeypot) where it "monitors" the passing Traffic for blacklisted items/trails (i.e. domain names, URLs and/or IPs). In case of a positive match, it sends the event details to the (central) Server where they are being stored inside the appropriate logging directory (i.e. LOG_DIR described in the Configuration section). If Sensor is being run on the same machine as Server (default configuration), logs are stored directly into the local logging directory. Otherwise, they are being sent via UDP messages to the remote server (i.e. LOG_SERVER described in the Configuration section).

👀 👉🏼 https://github.com/stamparm/maltrail#introduction

👀 👉🏼 ipsum:
https://github.com/stamparm/ipsum

#stamparm #maltrail #ipsum #tool #malicious #detection #blacklist
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag

Thailand shop installs system to keep doors shut to customers not wearing masks

Twitter user Niall Harbison shared a video of the shop which has a door fitted with a machine that scans the face of the customer for a mask and also records the body temperature. The doors open only if the machine detects a mask and the customer doesn’t have a fever.

👀 👉🏼 https://indianexpress.com/article/trending/trending-globally/thailand-face-mask-detection-machine-6716001/

👀 👉🏼 https://nitter.net/NiallHarbison/status/1312668730791403520#m

#thailand #corona #facemask #detection #video
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag

#Google has open sourced one of their #AI use #detection tools

https://news.itsfoss.com/google-synthid-text-open-source/

Chasing Your Tail (CYT)

https://github.com/ArgeliusLabs/Chasing-Your-Tail-NG

A comprehensive #WiFi probe request analyzer that monitors and tracks wireless devices by analyzing their probe requests. The system integrates with #Kismet for packet capture and WiGLE API for #SSID #geolocation analysis, featuring advanced #surveillance #detection capabilities.


Features

    Real-time Wi-Fi monitoring with Kismet integration
    Advanced surveillance detection with persistence scoring
    Automatic GPS integration - extracts coordinates from Bluetooth GPS via Kismet
    GPS correlation and location clustering (100m threshold)
    Spectacular KML visualization for Google Earth with professional styling and interactive content
    Multi-format reporting - Markdown, HTML (with pandoc), and KML outputs
    Time-window tracking (5, 10, 15, 20 minute windows)
    WiGLE API integration for SSID geolocation
    Multi-location tracking algorithms for detecting following behavior
    Enhanced GUI interface with surveillance analysis button
    Organized file structure with dedicated output directories
    Comprehensive logging and analysis tools

Requirements
    Python 3.6+
    Kismet wireless packet capture
    Wi-Fi adapter supporting monitor mode
    Linux-based system
    WiGLE API key (optional)