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GLOBAL BOTNET DETECTOR
BRENTON MALLEN
ABOUT ME
HI THERE!
Data Scientist
at Distil Networks
Email: brenton.mallen@distilnetworks.com
Blog: carpefridiem.wordpress.com
Twitter: @BrentonMallen
2
MOTIVATION
WHAT’S MY MOTIVATION?
▸ Product of an investigation of a DDoS attack on a customer
▸ Wanted a means to be alerted
▸ Wanted a means to identify a group of users potentially
responsible
3
BOTS & BOTNETS
WHAT ARE THESE BOTS I KEEP HEARING ABOUT?
▸ Automated code that
pretends to be human
▸ Used to traverse them
internets
▸ Not all bots are bad
4
BOTS & BOTNETS
IS A BOT REALLY ALL THAT DANGEROUS?
▸ Botnets can cause damage:
▸ DDoS
▸ Mass Security Breaches
▸ Mass Data Theft
5
ANALOGY
HOW ABOUT AN ANALOGY?
6
ANALOGY
HOW ABOUT AN ANALOGY?
7
ANALOGY
Casual Traffic Botnet Traffic
8
HOW ABOUT AN ANALOGY?
BOTNET DETECTOR
WHAT ARE THE GOALS OF A BOTNET DETECTOR?
▸ Detect
▸ Presence of a Botnet
▸ Identify
▸ List of Suspects
9
BOTNET DETECTOR
WHAT TOOLS DO WE USE?
▸ Python
▸ Boto
▸ Numpy
▸ AWS
▸ Hadoop
▸ Hive
▸ M-R Streaming
1.25 Billion Logs = 600 GB of Data
per Day
10
BOTNET DETECTOR
HOW DO WE DETECT A BOTNET?
▸ Part 1: Detect - For a given site, for each time window:
AGGREGATE COUNTRY
TRAFFIC
CHECK FOR
COORDINATED TRAFFIC
PRODUCE ALERT
11
SITE TRAFFIC
WHAT DOES THE TRAFFIC LOOK LIKE?
Time
RequestCount
BOTNET DETECTION
CROSS-COUNTRY CORRELATION
TIME WINDOW
Country (A-Z)
Country(A-Z)
1.0
0.0
-1.0
13
BOTNET DETECTION
CROSS-COUNTRY CORRELATION
TIME WINDOW
Country(A-Z)
Country (A-Z)
1.0
0.0
-1.0
14
BOTNET DETECTION
CROSS-COUNTRY CORRELATION
TIME WINDOW
Country(A-Z)
Country (A-Z)
1.0
0.0
-1.0
15
BOTNET DETECTION
CROSS-COUNTRY CORRELATION
TIME WINDOW
Country(A-Z)
Country (A-Z)
1.0
0.0
-1.0
16
BOTNET DETECTION
CROSS-COUNTRY CORRELATION
TIME WINDOW
Country(A-Z)
Country (A-Z)
1.0
0.0
-1.0
17
BOTNET DETECTION
CASUAL TRAFFIC
Country (A-Z)
Country(A-Z)
Country (A-Z)
SUSPICIOUS TRAFFIC
1.0
0.0
-1.0
CROSS-COUNTRY CORRELATION
18
CORRELATION COEFFICIENT 2D-HISTOGRAM
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
CASUAL TRAFFIC
Site A Site B
Time Time
19
BOTNET DETECTION
CORRELATION COEFFICIENT 2D-HISTOGRAM
0 —>
-1.0
0.0
1.0
CASUAL TRAFFIC
Site A Site B
Time Time
20
-1.0
0.0
1.0
BOTNET DETECTION
CORRELATION COEFFICIENT 2D-HISTOGRAM
0 —>
CASUAL TRAFFIC
Site A Site B
21
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
22
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
23
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
24
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
25
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
26
BOTNET DETECTION
0 —>
-1.0
0.0
1.0
Site C
Time
27
BOTNET DETECTION
ALERT PARAMETER
Time
Energy
28
BOTNET DETECTION
ALERT PARAMETER
Alert Threshold
Time
Energy
29
IDENTIFY PARTICIPANTS
HOW DO WE FIND THOSE RESPONSIBLE?
▸ Part 2: Identify Participants
▸ From Detection Phase
▸ Times of Alerts
▸ Participating Countries
▸ Requires User Fingerprint
▸ ID Based on Various User Configuration Parameters
30
IDENTIFY PARTICIPANTS
HOW DO WE FIND THOSE RESPONSIBLE?
ISOLATE USERS IN
COUNTRIES
CHECK FOR MULTI-
COUNTRY PRESENCE
FIND COORDINATED
USERS
31
IDENTIFY PARTICIPANTS
Argentina - South AfricaIndonesia - Russian Federation
0.77
0.94
RequestCounts
Time
Threat
Score
32
A1
A2
B1
B2
IDENTIFY PARTICIPANTS
WHAT DOES THE FINAL OUTPUT LOOK LIKE?
ID Threat Score
007E6ABE-A48C-3DE5-81E0-CBECBC2C96AB 0.82
07EF4DBE-EC0D-3BCE-A5BA-5910FF2457F5 0.97
0CCA9DA5-D63D-34E9-85A1-55154E5480E2 0.96
17C00FD8-E931-3789-AAC4-ED004C9143DB 0.90
22533F87-4B97-356A-95A4-84D5A8841F63 0.78
2E1C87C1-90BF-37BB-9A33-C482038AEE57 0.92
2F91B34E-AB15-389B-BCB6-8D913135D 0.95
3F6B5DF3-607E-3F1F-8050-2932B11D9E8A 0.94
46069A1E-F077-3F78-870A-C9BD7A0E1740 0.81
58A8DB25-2B99-3D2F-BA6D-50D1A8CFF3E9 0.77
58CBD814-CAC1-3644-8AB9-99A3C07A8E8F 0.70
6336DAC7-6508-3E79-9D99-37034A7C2E3F 0.83
655A6266-D316-360C-BAC1-76F26F3C0643 0.72
66C3A2B1-2953-3848-882C-591224C77E33 0.91
RECAP
WHAT DID WE DO?
DETECTED THE
PRESENCE OF A
BOTNET
SCRUTINIZED USERS
FROM PARTICIPATING
COUNTRIES
PRODUCED A LIST OF
SUSPECT USERS
PERFORMANCE
HOW DOES IT PERFORM?
▸ Prototype - Looks at Past Data
▸ Applied to an attack investigation
▸ 10 alerts over the month in question
▸ 100% of responsible users*
▸ Botnet Limited to Cross-Country
▸ Lacks Sub-Country insight
* Deemed responsible by the customer
35
FUTURE WORK
WHERE DO WE GO FROM HERE?
▸ Integrate into ML product
▸ Extract Features from Suspects
▸ Address Pitfalls
▸ Inefficiencies Due to Sparsity, Intra-country Activity
▸ 24/7 Streaming Process Across all customer sites
▸ Utilize New Tools
▸ Spark, storm, etc.
▸ Internal Platform
36
QUESTIONS?
THANK YOU
37
Global Botnet Detector

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