Kusto Detective Agency: Challenge 4 – Ready to play?


Just when you thought these challenges couldn’t get any cooler along comes your very own nemesis and a multi-part puzzle taking you on a street tour of New York City.

General advice

First, we need to import the data ourselves this time around, using Ingest from Blob under our data blade, you can also change the column name I used “Primes”
Calculating the prime numbers can be a little tricky as our free ADX cluster requires us to be clever with our query in order to allow it to complete, luckily, we get a free lesson on “special primes”

Challenge 4: Ready to play?

This challenge has two parts and we’ll look at them in turn, first we need to identify a specific prime number and then use that to get the second clue and then we have to find a specific area in New York City,

Query Hint Part 1
Calculating the largest special prime under 100M can be done in a variety of ways, the trick is working within the limited capacity of our free ADX cluster. KQL commands that are useful are serialize, prev, next and join
Ready to play? challenge text - Part 1

Hello. I have been watching you, and I am pretty impressed with your abilities of hacking and cracking little crimes.
Want to play big? Here is a prime puzzle for you. Find what it means and prove yourself worthy.


Start by grabbing Prime Numbers from
https://kustodetectiveagency.blob.core.windows.net/prime-numbers/prime-numbers.csv.gz and educate yourself on Special Prime numbers (https://www.geeksforgeeks.org/special-prime-numbers), this should get you to
https://aka.ms/{Largest special prime under 100M}

Once you get this done – you will get the next hint.

El Puente.

Query challenge 4 - Part 1

//Method 1 – This query will calculate the largest prime under 100M using the Sieve of Eratosthenes method to test each prime

| serialize
| order by Primes asc
| extend prevA = prev(Primes,1)
| extend NextA = next(prevA,1)
| extend test =  prevA + NextA + 1
| where test % 2 != 0 // skip even numbers
| extend divider = range(3, test/2, 2) // divider candidates
| mv-apply divider to typeof(long) on
  summarize Dividers=countif(test % divider == 0) // count dividers
| where Dividers == 0 // prime numbers don’t have dividers
| where test < 100000000 and test > 99999000
| top 1 by test

//Method 2 – This query will calculate the largest prime under 100M by comparing special primes to the data set of all prime numbers

| serialize
| project specialPrime = prev(Primes) + Primes + 1
| join kind=inner (Challenge4) on $left.specialPrime == $right.Primes
| where specialPrime < 100000000
| top 1 by Primes desc

Now that we have our prime number we can move on to part 2
Largest special prime under 100m

The number we want is 99999517 so we can now head over to http://aka.ms/99999517

A-ha a message from our nemesis and we need to meet them in a specific area marked by certain types of trees!

Ready to play? challenge text - Part 2

Well done, my friend.
It's time to meet. Let's go for a virtual sTREEt tour...
Across the Big Apple city, there is a special place with Turkish Hazelnut and four Schubert Chokecherries within 66-meters radius area.
Go 'out' and look for me there, near the smallest American Linden tree (within the same area).
Find me and the bottom line: my key message to you.

El Puente.

PS: You know what to do with the following:


.execute database script <|
// The data below is from https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh 
// The size of the tree can be derived using 'tree_dbh' (tree diameter) column.
.create-merge table nyc_trees 
       (tree_id:int, block_id:int, created_at:datetime, tree_dbh:int, stump_diam:int, 
curb_loc:string, status:string, health:string, spc_latin:string, spc_common:string, steward:string,
guards:string, sidewalk:string, user_type:string, problems:string, root_stone:string, root_grate:string,
root_other:string, trunk_wire:string, trnk_light:string, trnk_other:string, brch_light:string, brch_shoe:string,
brch_other:string, address:string, postcode:int, zip_city:string, community_board:int, borocode:int, borough:string,
cncldist:int, st_assem:int, st_senate:int, nta:string, nta_name:string, boro_ct:string, ['state']:string,
latitude:real, longitude:real, x_sp:real, y_sp:real, council_district:int, census_tract:int, ['bin']:int, bbl:long)
with (docstring = "2015 NYC Tree Census")
.ingest async into table nyc_trees ('https://kustodetectiveagency.blob.core.windows.net/el-puente/1.csv.gz')
.ingest async into table nyc_trees ('https://kustodetectiveagency.blob.core.windows.net/el-puente/2.csv.gz')
.ingest async into table nyc_trees ('https://kustodetectiveagency.blob.core.windows.net/el-puente/3.csv.gz')
// Get a virtual tour link with Latitude/Longitude coordinates
.create-or-alter function with (docstring = "Virtual tour starts here", skipvalidation = "true") VirtualTourLink(lat:real, lon:real) { 
	print Link=strcat('https://www.google.com/maps/@', lat, ',', lon, ',4a,75y,32.0h,79.0t/data=!3m7!1e1!3m5!1s-1P!2e0!5s20191101T000000!7i16384!8i8192')
// Decrypt message helper function. Usage: print Message=Decrypt(message, key)
.create-or-alter function with 
  (docstring = "Use this function to decrypt messages")
  Decrypt(_message:string, _key:string) { 
    let S = (_key:string) {let r = array_concat(range(48, 57, 1), range(65, 92, 1), range(97, 122, 1)); 
    toscalar(print l=r, key=to_utf8(hash_sha256(_key)) | mv-expand l to typeof(int), key to typeof(int) | order by key asc | summarize make_string(make_list(l)))};
    let cypher1 = S(tolower(_key)); let cypher2 = S(toupper(_key)); coalesce(base64_decode_tostring(translate(cypher1, cypher2, _message)), "Failure: wrong key")

Using the census data, we now need to figure out the location in the clue, luckily, it’s only a KQL query away

Query Hint - Part 2
Getting the right size area can be tricky, a KQL command that will be extremely helpful will be geo_point_to_h3cell

Query challenge 4 - Part 2

//This query will filter a specific size area until it makes the set of trees given in the clue

let locations =
| extend h3cell = geo_point_to_h3cell(longitude, latitude, 10)
| where spc_common == “‘Schubert’ chokecherry”
| summarize count() by h3cell, spc_common
| where count_ == 4
| summarize mylist = make_list(h3cell);
let final =
| extend h3cell = geo_point_to_h3cell(longitude, latitude, 10)
| where h3cell in (locations)
|where spc_common ==  “Turkish hazelnut” or spc_common == “American linden”
| summarize count() by h3cell, spc_common
| where spc_common == “Turkish hazelnut” and count_ ==1
| project h3cell;
| extend h3cell = geo_point_to_h3cell(longitude, latitude, 10)
| where h3cell in (final)
| where spc_common == “American linden”
| top 1 by tree_dbh asc
| project latitude, longitude
| extend TourLink = strcat(‘https://www.google.com/maps/@’, latitude, ‘,’, longitude, ‘,4a,75y,32.0h,79.0t/data=!3m7!1e1!3m5!1s-1P!2e0!5s20191101T000000!7i16384!8i8192’)

Now that we have a location, we’re not done yet and here’s where the fun really starts, using our generated link will take us on a “Tour of the City” and give us a google maps street view link. Have a look around for our mysterious “El Puente” you may need to walk around a little bit.

Now that we’ve found the message, there’s one more thing we need to do and that’s to use the decrypt function to figure out the message from out detective portal, this part was a little tricky and took a few tries to get the right string to use.

Decryption Key

Using the mural the phrase we are looking for is “ASHES to ASHES”

There we have it, another secret message! Keep a hold of this answer as you’ll need it to complete the final challenge.

Well done Detective, we’ve been on quite the journey. See you in the next challenge!


3 thoughts on “Kusto Detective Agency: Challenge 4 – Ready to play?

  1. Pingback: Kusto Detective Agency: Challenge 3 - Bank robbery! - OpsMan

  2. Pingback: Kusto Detective Agency: Hints and my experience - OpsMan

  3. Pingback: Kusto Detective Agency: Challenge 5 - Big heist - OpsMan

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