Learning and Recognizing the Places We Go

After initially trying to port this tool in Visual Studio, I ended up porting this on Linux using mingw-gcc. I have not tried to compile for x86, or using VS (where there will be issues due to gcc/VS-only C issues).

Tapeta «Beaconprint»: modele winobluszczu, kolekcje cegieł i kaw, recenzje

This is a Beacon Object File implementation of Yaxser’s Backstab for use with Cobalt Strike.

Additional work would alter the BeaconPrint statements to utilize something neater like Trustedsec’s method.

A few changes were made to the code during the port of the original:

1. The ProcExp driver is no longer stored/loaded as a resource, it is a hardcoded byte array in resource.c
2. There were several memory leaks in the original code that I found and resolved

After initially trying to port this tool in Visual Studio, I ended up porting this on Linux using mingw-gcc. I have not tried to compile for x86, or using VS (where there will be issues due to gcc/VS-only C issues).

To compile using gcc:

x86_64-w64-mingw32-gcc -o backstab.x64.o -Os -c main.c -DBOF -D_UNICODE 

1. First and foremost, Yaxser and his cool tool: https://github.com/Yaxser/Backstab
2. Trustedsec for his CS-Situational-Awareness-BOF repo which was a huge help during the porting process. I used snippets of his code in this project and I highly recommend anyone who is getting into writing BOF’s check the repo out: https://github.com/trustedsec/CS-Situational-Awareness-BOF

Learning and Recognizing the Places We Go

Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someone’s personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.

Keywords

• Global Position System
• Mobile Device
• Data Collector
• Ubiquitous Computing
• Visit Frequency

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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