Wordlist Maroc

Wordlist Maroc


Wordlist Maroc

This chapter provides a detailed and careful assessment of the Lingua Franca lexicon as it appears in the Dictionnaires Lingua Franca. Section 5.1 discusses the etymological sources of the Lingua Franca lexicon. Section 5.2 focuses on the core vocabulary of the Swadesh wordlists, while Section 5.3 addresses the layered nature of the Lingua Franca lexicon, focusing on its Romance and exotic (Turkish and Arabic) components. Section 5.4 scrutinizes selected typological features of the Lingua Franca lexicon in relation to properties which have been identified as characteristic of pidgin lexicons, such as typical vocabulary sources and sizes; and discusses features that specifically characterize the lexicon of Lingua Franca: doublets, suppletion patterns, and selected aspects of lexical typology and idiomatic structure. It is shown throughout that the lexicon of the Dictionnaires Lingua Franca displays detailed, specific, and interlocking continuity with the lexical and idiomatic features of its Romance lexifiers. Section 5.5 recapitulates the main points.

Topps! I know we’ve been down this road before, but it’s just fascinating how so many times you get that exact same story. Here’s why.
Now your main problem is….
You have a file with x amount of entries, and the majority are bad. Typically the best you can do is take the long odds, and look for the commonalities, even though many are bad.
That sucks, but you end up with many instances of “DEFAULT_AP” You will want to remove those from the results, since they are default access points.
Now your problem isn’t solving access points, it’s using them. If you are able to find enough of the good ones, you can start mass reducing the total amount of entries to the point where you can run something like this:
aircrack-ng -e
-w hs -i wlan0mon0 -P wlan0mon0-1 -f Neighborswifi_B8-87-1F-54-CD-E8.cap -c
-r 50 -w wordlists/example.txt
-t wlan0mon0 -h
-f output.cap (this is a great tool for this purpose, it will show you how many key-pairs in the wordlist and how many times you guessed, these help you determine how many good points you have from the whole wordlist)
-i wlan0mon0 -f
-c 4
-o output.cap

Finally, this will write 4 output files, each with a default AP, a passphrase AP, the passphrase guess, and a guess AP. Now you can reduce the amount of the bad access points while increasing the number of good guesses. This can lead you to a usable wordlist. If you want to save it as a passphrase, that’s easy too. (I’ll add that to my aircrack-ng tool soon)

1) Having a wordlist, is only the first part of the search. When your on the road, you may not have a full wordlist with you, but you still have a very useful tool called the Python website. If you are comfortable using python, this is well worth a download. I can’t imagine the times I’ve used these resources to find new access points, or to list all of the access points on a specific network. (Its a rabbit hole we dont want to go down if we dont want to know
“I hate wasting time, I hate wasting time, I hate wasting time” (Hare Krishna mantra… me too)

Please tell everyone out there about these resources! Everyone should have more ways to track and locate these networks! You just never know where the next great Wi-Fi access point is!




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