BotGraph: Large Scale Spamming Botnet Detection

BotGraph: Large Scale Spamming Botnet Detection Yao Zhao EECS Department Northwestern University

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

Web-Account Abuse Attack Server zombie User/Pwd Captcha solver RDSXXTD3

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

Aggressive signups Signup data Sendmail data Suspicious clusters Spamming botnets Signup botnets System Architecture EWMA based change detection Verification & prune Verification & prune Random graph based clustering Graph generation Login graph Login data Run on DryadLinq clusters Output locally

History Based Change Detection Large prediction error Back to normal

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

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 dynamic 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 several different IPs in different ASes

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

Random Graph Theory • Random Graph G(n,p) • n nodes and a pair of nodes has an edge with probability p • Theorem • A graph generated by G(n, p) has average weight d = n·p. • If d < 1, then with high probability the largest component in the graph has size less than O(log n). • If d > 1, with high probability the graph will contain a giant component with size at the order of O(n).

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 (currently not used) bot-users in the queue. • Strategy 3 • Similar to the second case, except that there is no limit on the number of bot-users a bot can request for one day.

Typical Bot-user Graphs • 10000 bot-users, 10-day activity, k = 20

Bot-user Detection Algorithm • Issues • Different bot-user groups may be connected (in the graph with weight threshold 2) • Shared bots • Shared bot-users • No fixed weight threshold T • Exceptions: exist large connected components formed by normal users • Detection Algorithm • Hierarchical algorithm to extract connected components • Pruning

Hierarchical Connected Component Extraction G T=2 A B T=3 C D T=4 E

Exceptions: Connected Subgraphs of Normal Users • Potential Reasons • Some web service providers login Hotmail accounts 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

Prune Good Groups • Email Sending Frequency • Normal users: generally don’t send many emails in average • Bot-users: to be efficient, send several spams every day • Email Size • Normal users: random size • Bot-users: (currently) similar email size

Prune Good Groups Bad: Good:

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

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

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)

Connected Component Extraction (A, B) (D, G) (B, C) (C, E) (C, D) (E, F) (B, G) (G, D) Local Algo (A, B) (D, G) (B, C) (B, E) (C, D) (E, F) (B, G) (B, D)

Connected Component Extraction (A, B) (D, G) (B, C) (B, E) (C, D) (E, F) (B, G) (B, D) Merge and local algo (A, B), (A, C), (A, E), (D, G) (B, D), (B, C), (B, G), (E, F)

Connected Component Extraction (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)

Connected Component Extraction • Analysis • M partitions and log(M) steps • Partition size ≤ N (number of users) • Overall communication overhead • O(N·log(M)) • Computational overhead

Detection of Signup Abuse

Detection by User-user Graph

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

Comparison with Complained Users • Ks : known spammer accounts signed up in the studied month • H : set of bot-users detected by EWMA

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

Validation of Sending Pattern

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 naming template from a regular expression pool • The regular expressions don’t quite match normal user names

False Positive Estimation (1) • Naming Score

False Positive Estimation (1) • Naming Score • A majority of the bot-user groups have close to 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 strictly follow the naming templates of their corresponding groups. • Take this 0.44% as false positive bound

False Positive Estimation (2) • Signup dates of the detected bot-users • Conservatively take all the accounts signed up before 2007 as legitimate • 0.08% bot-users were signed up before year 2007 • Among all the accounts in the 2008-dataset, about 59.1% of accounts were signed up before 2007 • False positive • Assuming normal users' behaviors don’t change • 0.08% / 59.1% = 0.13%

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)

Related Work • Botnet Detection • Hard in general • HoneyNet • Content-based Spam Detection • Bayesian filtering, AutoRE • Countermeasures: good words, image • Behavior-based Spam Detection • SpamTracker

Conclusions • BotGraph • History-based change detection on Signup • Graph-based component to detect stealthy bot-user logins • Parallel Algorithms on DryadLINQ • Quick process of huge Hotmail log • Detection • Detect more than 26M bot-accounts in two-month log • Low false positive

Q & A? Thanks!

BotGraph: Large Scale Spamming Botnet Detection