Hackers Arise Wi-Fi Radar

Welcome back, aspiring cyberwarriors!

One of our advanced student who goes by the handle Mike211 has developed a Wi-Fi scanning script that we want to share with all of you. What makes this script different and special is it’s ability to locate the Wi-Fi access points (AP) in your area.

I”ll let him introduce his new tool below!

In the Wi-Fi domain, raw signal strength and MAC identifiers can reveal more than just the presence of networks — they can open a path to estimating physical distance, mapping access points, and even executing wardriving missions or indoor localization without GPS. If you’ve ever wanted to push the boundaries of Wi-Fi auditing beyond mere detection, Hackers Arise Radar is your next-level tool.

Why this Tool is Game Changing

Just like Wigle.net collects crowdsourced location data of APs, this project allows you to discover and map Wi-Fi access points in real-time using only your Linux laptop or USB Wi-Fi adapter.

With this tool, you’ll get:

– Continuous scans over 2.4 GHz, 5 GHz, 6 GHz, or all bands
– Fully automated interface setup (monitor mode, regulatory domain, TX power)
– Filtered and smoothed RSSI values with Kalman filtering
– On-demand calibration for RSSI-to-distance
– Spring-model map generation to visualize spatial relationships
– Exportable logs, visuals, and calibration profiles for future use

Whether you’re driving through a city, walking indoors, or performing a pentest, you can leverage this tool for actionable location data.

How it Works – Step by Step

Step #1. Launch & Configuration

Start the script:

kali > sudo python3 Hackers_Arise_Radar.py

You’ll be greeted with a colorful terminal interface that guides you through:

– Selecting your Wi-Fi interface
– Choosing the operational environment (indoor, urban, open space)
– Selecting scan band (2.4 GHz / 5 GHz / 6 GHz / All)

No need to manually enable monitor mode – the script automatically puts your adapter into monitor mode, sets the regulatory domain, and adjusts TX power based on your choices.

Step #2. Real-Time Wi-Fi Scanning

The script uses airodump-ng behind the scenes to:
– Continuously scan surrounding Wi-Fi networks
– Record BSSID, SSID, RSSI, channel, frequency band
– Stream live updates through a structured CSV output for parsing and analysis

Step #3. RSSI Filtering & Analytics

To reduce RSSI noise, the script implements a Kalman filter This Kalman filter:

– Smooths out transient signal spikes
– Creates a rolling average of RSSI per BSSID
– Improves distance estimation consistency

Step #4. Estimating Distance from RSSI

The tool calculates the distance using a log-distance path loss model such as:

d = 10^((TX_power – RSSI) / (10 * n))

Where:
– TX_power and path-loss exponent n are customizable or calculated through calibration
– RSSI is dynamically filtered
– Distance is measured in meters

Step #5. Calibration Engine

The included calibration module lets you:

– Input known RSSI and real-world distances
– Fit an optimized curve per BSSID
– Automatically store TX power, path-loss exponent, and R² fit for reuse
– Flag poorly calibrated networks with suggestions

Step #6. Visual Mapping – Spring Model Layout

Once enough data is gathered, the tool uses a spring-model algorithm to create a map:
– Nodes (BSSIDs) are arranged based on estimated distances
– Forces push/pull the layout into geometric balance
– Labels show SSIDs, bands, and estimated distance in meters

Step #7. Regulatory & Power Tuning Mode

The tool isn’t just a scanner — it includes a dedicated utility mode to:

– Set regulatory domain (iw reg set <country_code>)
– Modify TX power (in dBm)
– Retrieve and display current wireless driver info
– Perform diagnostics before scanning

Focus Mode: Tracking a Single Access Point

Sometimes you just need to follow one Wi-Fi target — whether it’s a rogue device, a signal beacon, or an access point you’re using for indoor positioning.

Hackers Arise Radar includes a specialized mode for scanning and tracking a single BSSID:

– Select a known access point from your previously scanned list
– The tool locks onto that specific MAC address using:
  airodump-ng –bssid <target> –channel <ch>
– RSSI values are filtered using a Kalman filter
– Distance estimation is updated in real-time using the calibration profile
– Live updates show proximity and confidence

Real World Use Cases

– Wardriving Missions: Continuous logs while driving
– Indoor Wireless Mapping: Signal-based AP triangulation, spatial layouts
– Security & Pentesting Recon: Detect new/rogue APs, estimate proximity
– Wi-Fi Optimization: Adjust regulatory domain / TX power, evaluate coverage
– Wireless Simulation & Testing: Simulate RSSI data with simulate_rss_matrix.py

Requirements & Setup

– Platform: Linux (Kali/Debian-based)
– Python: 3.7+
– Privileges: sudo required
– External Tools: aircrack-ng, iw, ip, ethtool
– Python Libraries: numpy, scipy, pandas, matplotlib, adjustText

Launch simply with:

kali> sudo python3 Hackers_Arise_Radar.py

No need to prep interfaces — the tool handles it all.

Summary

Hackers Arise Radar is more than just a scanner. It is a fully interactive system for Wi-Fi discovery, proximity estimation, map generation, and interface configuration — all controlled through an elegant terminal menu.

Built for hackers, engineers, educators, and hobbyists, this tool empowers you to:
– Visualize your wireless environment
– Optimize TX power and regulatory settings
– Log and export clean data
– Build wireless maps with zero GPS

Start scanning smarter — not harder.

For more information on this unique and powerful scanner, see our Wi-Fi Hacking training.

The post Python Basics for Hackers: Building a Wi-Fi Scanner Capable of Locating the Position of Local AP’s first appeared on Hackers Arise.

Welcome back, my aspiring cyberwarriors!

The quick answer is “Yes!”.

It might seem like science fiction, but now we have the capability to “see” through walls and track the location and movement of targets. This is thanks to new technological developments in both artificial intelligence and SDR. Remember, Wi-Fi is simply sending and receiving radio signals at 2.45Ghz. If an object is in the way of the signal, it bounces, bends and refracts the signal. This perturbing of the signal can be very complex but advances in machine learning (ML) and AI now make it possible to to collect and track those changes in the signal and determine if it’s a human, dog, or an intruder. This is the beginning of something exciting, and quite possibly, malicious.

This is one more reason why we say that SDR (Signals Intelligence) for Hackers is the leading edge of cybersecurity!

The Science Behind Wi-Fi Sensing

How It Works

• Wi-Fi signals are electromagnetic waves that can pass through common wall materials like drywall, wood, and even concrete (with some signal loss).
• When these signals encounter objects, especially humans, they reflect, scatter, and diffract.
• By analyzing how Wi-Fi signals bounce back, it’s possible to detect the presence, movement, and even the shape of people behind walls.

Key Concepts

• Phase and Amplitude: The changes in phase and amplitude of the Wi-Fi signal carry information about what the signal has encountered.
• Multipath Propagation: Wi-Fi signals reflect off multiple surfaces, producing a complex pattern that can be decoded to reveal movement and location.
• DensePose & Neural Networks: Modern systems use AI to map Wi-Fi signal changes to specific points on the human body, reconstructing pose and movement in 3D.

The Hardware

You don’t need military-grade gear. Here’s what’s commonly used:

• Standard Wi-Fi Routers: Most experiments use commodity routers with multiple antennas.
• Software-Defined Radios (SDRs): For more control and precision, SDRs like the HackRF or USRP can be used (see our tutorials and trainings on SDR for Hackers)
• Multiple Antennas: At least two, but three or more improves accuracy and resolution.

The Software

Data Collection

• Transmit & Receive: One device sends out Wi-Fi signals, another listens for reflections.
• Channel State Information (CSI): This is the raw data showing how signals have changed after bouncing off objects.

Processing

• Signal Processing: Algorithms filter out static objects (walls, furniture) and focus on moving targets (people).
• Neural Networks: AI models such as DensePose map signal changes to body coordinates, reconstructing a “pose” for each detected person

Wi-Fi Sensing in Action

Step 1: Set Up Your Equipment

• Place a Wi-Fi transmitter and receiver on opposite sides of the wall.
• Ensure both devices can log CSI data. Some routers can be flashed with custom firmware (e.g., OpenWRT) to access this.

Step 2: Collect CSI Data

• Use tools like Atheros CSI Tool or Intel 5300 CSI Tool to capture the raw signal data.
• Move around on the far side of the wall to generate reflections.

Step 3: Process the Data

• Use Python libraries or MATLAB scripts to process the CSI data.
• Apply filters to remove noise and static reflections.
• Feed the cleaned data into a pre-trained neural network (like DensePose) to reconstruct human poses

Step 4: Visualize the Results

• The output can be a 2D or 3D “stick figure” or heatmap showing where people are and how they’re moving.
• Some setups can even distinguish between individuals based on movement patterns.

Limitations and Considerations

• Wall Material: Thicker or metal-reinforced walls reduce accuracy.
• Privacy: This technology raises major privacy concerns—anyone with the right tools could potentially “see” through your walls.
• Legality: Unauthorized use of such technology may violate laws or regulations.

Real-World Applications

• Security: Detecting intruders or monitoring restricted areas. Companies like TruShield are offering commercial home security systems based upon this technology.
• Elder Care: Monitoring movement for safety without cameras.
• Smart Homes: Automating lighting or HVAC based on occupancy.
• Law Enforcement: Law enforcement agencies can detect and track suspects in their homes
• Intelligence Agencies: Can Use this technology to track spies or other suspects.

Summary

Wi-Fi sensing is a powerful, rapidly advancing field. With basic hardware (HackRF) and open-source tools, it’s possible to experiment with through-wall detection. This opens a whole new horizon in Wi-Fi Hacking and SDR for Hackers.

For more on this technology, attend our upcoming Wi-Fi Hacking training, July 22-24. If you are interested in building this device, look for our 2026 SDR for Hackers training.

As always, use this knowledge responsibly and be aware of the ethical and legal implications.

The post Can Hackers “See” Inside Your Home Using Wi-Fi to Track Your Location and Movement? first appeared on Hackers Arise.