u/randomXperson__

2. Wi-Fi probe request harvesting and behavioral profiling.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

2. Wi-Fi probe request harvesting and behavioral profiling.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

Wi-Fi probe request harvesting and behavioral profiling.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

Wi-Fi probe request harvesting and behavioral profiling.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

Every phone constantly broadcasts "probe requests" — asking nearby air "are you my saved network?" — and these contain the SSIDs of every network the phone has ever connected to. Any laptop with a WiFi card that supports monitor mode (most do; check with iw list) can capture these passively with no hardware purchase. You'd put your card in monitor mode with airmon-ng, capture probe requests, and build a real-time analysis dashboard that: identifies unique devices (by MAC, with clustering for randomized MACs using timing correlation), infers their history (home network, workplace, coffee shops they've visited), tracks movement patterns over time, and demonstrates that MAC randomization is beatable through timing and SSID correlation attacks. Then you build the defensive tool: a detector that alerts when a device is being actively probed or tracked. Completely free. Very demonstrable in any public space with permission.

is it doable or not? Im gonna do it for my final year project

I am planning to do a project on gait based authentication for mobile phones for my final year project. I'm thinking of doing it by Authenticate smartphone users continuously

by analyzing how they walk using:

→ Accelerometer

→ Gyroscope

by taking x,y &z axis movements of the phone and training the model based on the users gestures.

But the major concern i face are that the authentication might fail when user walks over stairs or other kinds of environments. Another problem i find is that when user travels on a vehicle. So in such cases a false positive of the authentication failure might occur and the major difficulty of all is that the training process. The datasets available for training the model is less and contains a few seconds of data. It might not be feasible for me to train the model on my own as well. I have never trained a model before and i dont know much about its outcomes. So is there any way i could do this project by eliminating the challenges?? Is there any alternate way which i could accomplish this project and showcase it??

Can unintentional physical emanations from consumer electronics be systematically captured and decoded using commodity hardware to reconstruct user activity — and what does this mean for digital forensics, privacy, and security?"

Cause i wanna do this for my final year project

I am planning to do a project on gait based authentication for mobile phones for my final year project. I'm thinking of doing it by Authenticate smartphone users continuously

by analyzing how they walk using:

→ Accelerometer

→ Gyroscope

by taking x,y &z axis movements of the phone and training the model based on the users gestures.

But the major concern i face are that the authentication might fail when user walks over stairs or other kinds of environments. Another problem i find is that when user travels on a vehicle. So in such cases a false positive of the authentication failure might occur and the major difficulty of all is that the training process. The datasets available for training the model is less and contains a few seconds of data. It might not be feasible for me to train the model on my own as well. I have never trained a model before and i dont know much about its outcomes. So is there any way i could do this project by eliminating the challenges?? Is there any alternate way which i could accomplish this project and showcase it??

I am planning to do a project on gait based authentication for mobile phones for my final year project. I'm thinking of doing it by Authenticate smartphone users continuously

by analyzing how they walk using:

→ Accelerometer

→ Gyroscope

by taking x,y &z axis movements of the phone and training the model based on the users gestures.

But the major concern i face are that the authentication might fail when user walks over stairs or other kinds of environments. Another problem i find is that when user travels on a vehicle. So in such cases a false positive of the authentication failure might occur and the major difficulty of all is that the training process. The datasets available for training the model is less and contains a few seconds of data. It might not be feasible for me to train the model on my own as well. I have never trained a model before and i dont know much about its outcomes. So is there any way i could do this project by eliminating the challenges?? Is there any alternate way which i could accomplish this project and showcase it??