Rating: 3.7
# Path of the suspect
**Category**: OSINT \
**Points**: 425
The first thing I did was Google what MCC, MNC, LAC, and CID were. Eventually,
I found a tool called https://opencellid.org that would find locations based on
these values.
So next I wrote a script ([parse.py](parse.py)) to parse the PDF text into some easily readable
JSON like so:
```json
[
{
"day": "11.11.2020",
"time": "12:04",
"mcc": 260,
"mnc": 3,
"lac": 52911,
"cid": 8961,
"rtype": "GSM"
},
{
"day": "11.11.2020",
"time": "13.09",
"mcc": 260,
"mnc": 6,
"lac": 206,
"cid": 1658726,
"rtype": "UMTS"
},
```
Next I wrote another script that would send a request to OpenCelliD and read the
location:
```python
import requests
import pprint
import json
import time
url = 'https://www.opencellid.org/ajax/searchCell.php'
with open('src.json', 'r') as f:
src = json.load(f)
locs = []
for cell in src:
payload = {
'mcc': cell['mcc'],
'mnc': cell['mnc'],
'lac': cell['lac'],
'cell_id': cell['cid']
}
response = requests.request('GET', url, params=payload)
print("Received loc: ", response.json())
locs.append(response.json())
time.sleep(5) # Avoid rate-limiting
with open('locs.json', 'w') as f:
f.write(json.dumps(locs, indent=4))
```
The format of the `locs.json` was like this:
``` json
[
{
"lon": "19.894857",
"lat": "49.91982",
"range": "4594"
},
{
"lon": "19.891884",
"lat": "50.019426",
"range": "1000"
},
```
Next I needed a way to plot these on a map, so I decided to use https://www.mapcustomizer.com/.
I wrote a short script ([proc.py](proc.py)) to convert `locs.json` into a format that it liked:
```python
import json
import matplotlib.pyplot as plt
with open('locs.json', 'r') as f:
locs = json.load(f)
with open('mapconv', 'w') as f:
for loc in locs:
f.write("{},{}\n".format(loc['lat'], loc['lon']))
```
Then I pasted the `mapconv` file contents into the "Bulk Entry" modal and got
this map:
Finally after several hours of work, the flag was `AFFCTF{IOTLL}`
