Smart homes are increasingly becoming common in our digital world! These smart home devices have become of the key targets of malicious hackers. This is largely due to their very weak security. In 2025, attacks on connected devices rose 400 percent, with average breach costs hitting $5.4 million
In this three-day class, we will explore and analyze the various security weaknesses of these smart home devices and protocols.
Course Outline
Introduction and Overview of Smart Home Devices
Weak Authentication on Smart Home Devices
RFID and the Smart Home Security
Bluetooth and Bluetooth LE vulnerabilities in the home
Wi-Fi vulnerabilities and how they can be leveraged to takeover all the devices in the home
LoRa vulnerabilities
IP Camera vulnerabilities
Zigbee vulnerabilities
Jamming Wireless Technologies in the Smart Home
How attackers can pivot from an IoT devices in the home to takeover your phone or computer
How to Secure Your Smart Home
This course is part of ourSubscriber Pro training package
As Internet of Things (IoT) devices continue to permeate every aspect of modern life, homes, offices, factories, vehicles, their attack surfaces have become increasingly attractive to adversaries. The challenge with testing IoT systems lies in their complexity: these devices often combine physical interfaces, embedded firmware, network services, web applications, and companion mobile apps into a [...]
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.
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