This study was conducted in Mayaguez – Puerto Rico, and an area of around 18 Km2 was covered, which were determined using the following classification of places:
· Main Avenues: Wide public ways that has hospitals, vegetation, buildings, on either side
· Open Places: Mall parking lots and public plazas
· Streets & Roads: Dense residential and commercial areas on both sides
Vendor Equipment Description
KEYSIGHT® N9343C Handheld Spectrum Analyzer
***For CSV Files:
You can open the CSV files on Microsoft Excel or any other. In these files you will find the following information in different columns:
- Individually the information of each of the 20 scanned frequencies (center frequency, bandwidth, power in dBm).
***For KML Files:
You can open simply this interactive files using the Google Earth Pro software by clicking on file, open, and selecting the desired KML file.
At this point, the interactive map must be located on the place of the points and showing several colored ellipses between green for the weakest power level, and red for the strongest. If you press each point, you can see the complete information of each location.
The frequencies used in this study are shown below:
Survey Frequencies (Bandwidth 4% --> Patch Antenna)
Central Frequency (MHz) / Band Description
7200 .csv files, each containing a 10 kHz recording of a 1 ms lasting 100 hz sound, recorded centimeterwise in a 20 cm x 60 cm locating range on a table. 3600 files (3 at each of the 1200 different positions) are without an obstacle between the loudspeaker and the microphone, 3600 RIR recordings are affected by the changes of the object (a book). The OOLA is initially trained offline in batch mode by the first instance of the RIR recordings without the book. Then it learns online in an incremental mode how the RIR changes by the book.
folder 'load and preprocess offline data': matlab sourcecodes and raw/working offline (no additional obstacle) data files
folder 'lvq and kmeans test': matlab sourcecodes to test and compare in-sample failure with and without LVQ
folder 'online data load and preprocess': matlab sourcecodes and raw/working online (additional obstacle) data files
folder 'OOL': matlab sourcecodes configurable for case 1-4
folder 'OOL2': matlab sourcecodes for case 5
folder 'plots': plots and simulations
The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used.