botgraph: large scale spamming botnet detection

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BotGraph: Large Scale Spamming Botnet Detection

Yao Zhao

EECS Department

Northwestern University

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Outline

• Motivation and Problem Definition

• BotGraph Algorithms– History based algorithm on Signup detection– Graph-based algorithm on login detection

• Parallel Implementation on DryadLINQ

• Detection Results

• Discussion

• Conclusion

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Web-Account Abuse Attack

zombieServer

Captcha solver

RDSXXTD3

User/Pwd

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Problems and Challenges

• Web-account Abuse– Signup abuse– Spam sending

• Challenges– Accuracy requirement– Stealthy (in terms of spam sending)– Large scale attack and huge Hotmail log data

• Our Behavior-based Solutions– Correlate bot-users by their activities and identify the

group properties– Design parallel algorithms on DryadLINQ to efficiently

process large data

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System Architecture

Login data

Login graph

Graph generation

Random graph based clustering

Verification & prune

Sendmail data

Spamming botnets

Suspicious clusters

Signup data

EWMA based change detection Aggressive

signups

Verification & prune

Signup botnets

Run on DryadLinq clusters Output locally

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Outline





• Discussion

• Conclusion

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History Based Change Detection

Large prediction

error

Back to normal

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EWMA based Change Detection

• EWMA (Exponentially Weighted Moving Average)– Yt : observation at time t, St : prediction at time t– St = α×Yt-1 + (1 - α)×St-1

• Large Prediction Error Implies Change (or Abnormal)– Et = Yt – St (Prediction error) – Rt = Yt / Max(St,ε) (Relative prediction error)

• Apply EWMA based Change Detection to Signup Time Series of Each IP Address

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Outline





• Discussion

• Conclusion

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Normal and Bot-user Behaviors

• General Behaviors of Normal Users– Login Hotmail from home and/or office– The account shares IPs in one AS with others if dyna

mic IP is used• General Behaviors of Bot-users

– A pool of bots (e.g. thousands) and a pool of bot-users (e.g. hundreds of thousands)

• Each bot hosts multiple bot-users– Bot-user assigned to different random bots every day

• Fixed binding is not adopted now– A pair of bot-users have large chance to share severa

l different IPs in different ASes

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User-user Graph

• Graph Model– A hotmail account => a node– A pair of accounts share IPs => an edge

• Edge weight = Number of different ASes the shared IPs belong to

• Consider edges with weight>1

• Key Observations– Bot-users form a giant connected component– Normal users do not form large connected component– Interpreted by the random graph theory

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Random Graph Theory

• Random Graph G(n,p) – n nodes and a pair of nodes has an edge with probabi

lity p

• Theorem– A graph generated by G(n, p) has average weight

d = n · p. – If d < 1, then with high probability the largest compon

ent in the graph has size less than O(log n). – If d > 1, with high probability the graph will contain a g

iant component with size at the order of O(n).

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Typical Bot-user Graphs

• Strategy 1– Bot-user accounts are randomly assigned to bots.

• Strategy 2– Keeps a queue of the bot-users. – A bot comes online and gets the top k available (curre

ntly not used) bot-users in the queue.

• Strategy 3– Similar to the second case, except that there is no limi

t on the number of bot-users a bot can request for one day.

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Typical Bot-user Graphs

– 10000 bot-users, 10-day activity, k = 20

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Bot-user Detection Algorithm

• Issues– Different bot-user groups may be connected (in the gr

aph with weight threshold 2)• Shared bots• Shared bot-users

– No fixed weight threshold T– Exceptions: exist large connected components forme

d by normal users• Detection Algorithm

– Hierarchical algorithm to extract connected components

– Pruning

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Hierarchical Connected Component Extraction

G

A B

T=2

C D

E

T=3

T=4

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Exceptions: Connected Subgraphs of Normal Users

• Potential Reasons– Some web service providers login Hotmail acc

ounts for users (e.g. Facebook, Linkedin)– National proxies– Cell phones (e.g. iPhone)– Tor (Onion routing)

• Solutions– Filter out some IPs– Prune potential good connected components

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Prune Good Groups

• Email Sending Frequency– Normal users: generally don’t send many ema

ils in average– Bot-users: to be efficient, send several spams

every day

• Email Size– Normal users: random size– Bot-users: (currently) similar email size

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Prune Good Groups

Bad:

Good:

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Prune Good Groups

• Metrics– s1: the percentage of users who send more

than 3 emails per day

– s2: the percentage of users who send out emails with similar size (peak detection)

• Pruning– Threshold of s1 is 0.8 (conservative and wide

margins around 0.8)

– s2 is used in validation

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Outline





• Discussion

• Conclusion

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Parallel Implementation on DryadLINQ

• EWMA Algorithm of Signup Abuse Detection– Partition data by IP (straightforward)

• Graph Construction– Two algorithms

• Connected Component Extraction– Divide and conquer

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Connected Component Extraction

• Partitions of Edges– (User1, User2, weight)

(A, B) (D, G)

(B, C) (C, E)

(C, D) (E, F)

(B, G) (G, D)

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(A, B) (D, G)

(B, C) (C, E)

(C, D) (E, F)

(B, G) (G, D)

(A, B) (D, G)

(B, C) (B, E)

(C, D) (E, F)

(B, G) (B, D)

Local Algo

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(A, B) (D, G)

(B, C) (B, E)

(C, D) (E, F)

(B, G) (B, D)

(A, B), (A, C), (A, E), (D, G)

(B, D), (B, C), (B, G), (E, F)

Merge and local algo

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(A, B), (A, C), (A, E), (D, G)

(B, D), (B, C), (B, G), (E, F)

(A, B), (A, C), (A, D), (A, E), (A, F), (A, G)

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• Analysis– M partitions and log(M) steps– Partition size ≤ N (number of users) – Overall communication overhead

• O(N·log(M))

– Computational overhead

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Outline





• Discussion

• Conclusion

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Detection of Signup Abuse

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Detection by User-user Graph

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Validations

• Manual Check– Verified by Hotmail group

• Comparison with Known Spamming Users– Complained Hotmail accounts

• Email Sending Patterns– Email size

• False Positive Estimation– Naming pattern– Signup time

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Comparison with Complained Users

• Ks : known spammer accounts signed up in the studied month

• H : set of bot-users detected by EWMA

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Comparison with Complained Users

• Ks : known spammer accounts that log in from at least 2 ASes

• L : set of bot-users detected by user-user graph

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Validation of Sending Pattern

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False Positive Estimation (1)

• Naming Pattern– Clear pattern in names of (current) bot-users

• E.g. w9168d4dc8c5c25f9

– Naming pattern score• The largest fraction of users that follow a single na

ming template from a regular expression pool• The regular expressions don’t quite match normal

user names

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• Naming Score

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• Naming Score– A majority of the bot-user groups have close t

o 1 naming pattern scores– A few small bot-user groups with scores lower

than 95%– In total, 0.44% of identified bot-users do not st

rictly follow the naming templates of their corresponding groups.

– Take this 0.44% as false positive bound

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• Signup dates of the detected bot-users– Conservatively take all the accounts signed u

p before 2007 as legitimate– 0.08% bot-users were signed up before year

2007– Among all the accounts in the 2008-dataset, a

bout 59.1% of accounts were signed up before 2007

– False positive • Assuming normal users' behaviors don’t change• 0.08% / 59.1% = 0.13%

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Outline





• Discussion

• Conclusion

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Evasion

• Signup detection– Be stealthy

• Login detection– Fixed binding

• Low utilization rate• Bot-accounts bound to one host are easy to be grouped

– Fixed AS assignment• Redefine the edge weight to consider IP prefix• Similar to fixed binding

– Be stealthy (sending as few emails as normal user)

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Related Work

• Botnet Detection– Hard in general– HoneyNet

• Content-based Spam Detection– Bayesian filtering, AutoRE– Countermeasures: good words, image

• Behavior-based Spam Detection– SpamTracker

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Conclusions

• BotGraph– History-based change detection on Signup– Graph-based component to detect stealthy bot-user lo

gins

• Parallel Algorithms on DryadLINQ– Quick process of huge Hotmail log

• Detection– Detect more than 26M bot-accounts in two-month log– Low false positive

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Q & A?

Thanks!

botgraph: large scale spamming botnet detection

Documents

botuser accounts

nowa pair of bot

usedgeneral behaviors

hundreds of thousandseach

multiple botusersbot

different ips

signup time series

probability p theorema