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A GRAPH-BASED METHOD FOR
CROSS-ENTITY THREAT DETECTION
Herman Kwong, Ping Yan
Salesforce
Account Takeover
Detection is Key
• Basic features
• Known signature
• Usage anomaly
Each with their weaknesses
CrossLinks
• Unexpected common features, across unrelated user
accounts / environments
• Features:
IP, location, time zone,
user agent, browser fingerprint,
user action sequence, ...
 A Graph-Based Method For Cross-Entity Threat Detection
Why Graph(X)?
• Why Graph?
– Classical pair-wise entity relationship measurement solutions require O(N2) computations
– Computation complexity dramatically reduced by localizing computations
– Highly extensible solution with a multigraph
• Why GraphX?
– Spark ecosystem
– Scalability and performance
– Advanced Graph algorithms
Graph-theoretical Techniques
• Graph analysis is of high interest in many social network contexts
– Proximity-based approaches
– Personalized pagerank: closeness of each node to the restart nodes
– Simrank: similarity of contextual structures
• Bridge-Node anomaly [Akoglu, et al 2015]
– Publication networks: authors from different research communities
– Financial trading networks: cross-sector traders
– Customer-product networks: cross-border products
– Network intrusion detection: cut-vertices indicating nodes accessing multiple
communities that they do not belong to
Bipartite Graph
Bipartite: Application access data directly makes a bipartite graph where
an edge represents V1 accessing V2
V1 V2
An endpoint coming
from 73.228.152.xxx
Salesforce
Multigraph Formulation
We can also formulate the relationship of application access data as a
multigraph where an edge between two entities represents some features
that the two entities have in common.
V1 V2
IP={73.228.152.xxx}
UserAgent={Mozilla/5.0 (Macintosh; Intel Mac OS X)}
SalesforceHeroku
Anomaly Detection by Graph Change Detection
• Our objective is to quickly discover changes in the access graph
over time
• Unexpected new cross-entity connections are of particular interest in
security detection problems
• A naïve detector and a community-based algorithm were proposed
for access anomaly detection with a graph
Naïve Detector
REFERENCE GRAPH (TRAINING) - RG
DETECTION GRAPH (TESTING) – DG
MERGE OF RG & DG - RGDG
ANOMALY GRAPH (DEGREE INFO) – AG
CONNECTIVITY GRAPH (ENV-TO-ENV) – CG
Detection:
//outDegRG: count of neighbors in test nodes
//outDegRGDG: count of neighbors of nodes in the combination of test
data and reference data
//We like to calculate the difference between the two degree
//properties : outDegRGDG – outDegRG
Edges in red: edges in
only the detection graph but
not the reference graph.
Edges in blue: edges in
both the detection graph
and the reference graph.
Naïve Detector
V2
V1
V4
V3
V5
(1, 1)
(1, 3)
Reference Graph Detection Graph
Anomaly Graph with Degree Info
V1
V1
V4
V2
V2
V3
V3
V5
Merge (RG, DG)
IP={73.228.152.xxx}
Salesforce
 A Graph-Based Method For Cross-Entity Threat Detection
2nd-Order Connectivity
X.JOIN(X)
Bipartite graph Connectivity graph
IP={73.228.152.xxx}
Salesforce
2nd-Order Anomaly Graph
Accessed entities by communitiesEndpoint features
Cross-cluster outDegree: 1
Cross-cluster outDegree: 2
2nd-Order Anomaly Detector
Step 1: self join RG on the feature-of-interest (e.g., IP) to get the
env-to-env connectivity graph.
Step 2: Build the Anomaly Graph as in the Naïve Detector algorithm (1st-
order anomalies).
Step 3*: collapse the cluster of nodes into a single node on the
Anomaly Graph.
Step 4: run the naive algorithm to get the updated node degrees to
identify 2nd-order anomalies.
*: ConnectedComponent to approximate clusters
Experiments
• Reference Graph (RG) - Number of vertices: 2,222,613
• RG - Number of edges 2,156,104
• Connectivity Graph (CG) - Number of vertices: 4,682
• CG - Number of edges: 8,534
• CG - Number of ConnectedComponents: 1146
• Number of 1st-order anomalies: ~700
• Number of 2nd-order anomalies: ~200
• Computing time: ~ 5 minutes on a Mac Air (1.7 GHz Intel Core i7, 8G memory)
http://g.recordit.co/TBqvNbqonf.gif
Toolkit for Interactive Analysis
Lightning
Opportunities
• GraphDB for real-time indexing and query
• Probabilistic edges to support complex semantics
• Clustering on probabilistic graph for community detection
References
[Akoglu et al 2015] Akoglu, Leman, Hanghang Tong, and Danai Koutra. "Graph based
anomaly detection and description: a survey." Data Mining and Knowledge Discovery29.3
(2015): 626-688.
[Ding et al 2012] Ding, Qi, et al. "Intrusion as (anti) social communication:
characterization and detection." Proceedings of the 18th ACM SIGKDD international
conference on Knowledge discovery and data mining. ACM, 2012.
THANK YOU.
hkwong@salesforce.com
pyan@salesforce.com ( @pingpingya)

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A Graph-Based Method For Cross-Entity Threat Detection

  • 1. A GRAPH-BASED METHOD FOR CROSS-ENTITY THREAT DETECTION Herman Kwong, Ping Yan Salesforce
  • 3. Detection is Key • Basic features • Known signature • Usage anomaly Each with their weaknesses
  • 4. CrossLinks • Unexpected common features, across unrelated user accounts / environments • Features: IP, location, time zone, user agent, browser fingerprint, user action sequence, ...
  • 6. Why Graph(X)? • Why Graph? – Classical pair-wise entity relationship measurement solutions require O(N2) computations – Computation complexity dramatically reduced by localizing computations – Highly extensible solution with a multigraph • Why GraphX? – Spark ecosystem – Scalability and performance – Advanced Graph algorithms
  • 7. Graph-theoretical Techniques • Graph analysis is of high interest in many social network contexts – Proximity-based approaches – Personalized pagerank: closeness of each node to the restart nodes – Simrank: similarity of contextual structures • Bridge-Node anomaly [Akoglu, et al 2015] – Publication networks: authors from different research communities – Financial trading networks: cross-sector traders – Customer-product networks: cross-border products – Network intrusion detection: cut-vertices indicating nodes accessing multiple communities that they do not belong to
  • 8. Bipartite Graph Bipartite: Application access data directly makes a bipartite graph where an edge represents V1 accessing V2 V1 V2 An endpoint coming from 73.228.152.xxx Salesforce
  • 9. Multigraph Formulation We can also formulate the relationship of application access data as a multigraph where an edge between two entities represents some features that the two entities have in common. V1 V2 IP={73.228.152.xxx} UserAgent={Mozilla/5.0 (Macintosh; Intel Mac OS X)} SalesforceHeroku
  • 10. Anomaly Detection by Graph Change Detection • Our objective is to quickly discover changes in the access graph over time • Unexpected new cross-entity connections are of particular interest in security detection problems • A naïve detector and a community-based algorithm were proposed for access anomaly detection with a graph
  • 11. Naïve Detector REFERENCE GRAPH (TRAINING) - RG DETECTION GRAPH (TESTING) – DG MERGE OF RG & DG - RGDG ANOMALY GRAPH (DEGREE INFO) – AG CONNECTIVITY GRAPH (ENV-TO-ENV) – CG Detection: //outDegRG: count of neighbors in test nodes //outDegRGDG: count of neighbors of nodes in the combination of test data and reference data //We like to calculate the difference between the two degree //properties : outDegRGDG – outDegRG
  • 12. Edges in red: edges in only the detection graph but not the reference graph. Edges in blue: edges in both the detection graph and the reference graph. Naïve Detector V2 V1 V4 V3 V5 (1, 1) (1, 3) Reference Graph Detection Graph Anomaly Graph with Degree Info V1 V1 V4 V2 V2 V3 V3 V5 Merge (RG, DG) IP={73.228.152.xxx} Salesforce
  • 14. 2nd-Order Connectivity X.JOIN(X) Bipartite graph Connectivity graph IP={73.228.152.xxx} Salesforce
  • 15. 2nd-Order Anomaly Graph Accessed entities by communitiesEndpoint features Cross-cluster outDegree: 1 Cross-cluster outDegree: 2
  • 16. 2nd-Order Anomaly Detector Step 1: self join RG on the feature-of-interest (e.g., IP) to get the env-to-env connectivity graph. Step 2: Build the Anomaly Graph as in the Naïve Detector algorithm (1st- order anomalies). Step 3*: collapse the cluster of nodes into a single node on the Anomaly Graph. Step 4: run the naive algorithm to get the updated node degrees to identify 2nd-order anomalies. *: ConnectedComponent to approximate clusters
  • 17. Experiments • Reference Graph (RG) - Number of vertices: 2,222,613 • RG - Number of edges 2,156,104 • Connectivity Graph (CG) - Number of vertices: 4,682 • CG - Number of edges: 8,534 • CG - Number of ConnectedComponents: 1146 • Number of 1st-order anomalies: ~700 • Number of 2nd-order anomalies: ~200 • Computing time: ~ 5 minutes on a Mac Air (1.7 GHz Intel Core i7, 8G memory)
  • 19. Toolkit for Interactive Analysis Lightning
  • 20. Opportunities • GraphDB for real-time indexing and query • Probabilistic edges to support complex semantics • Clustering on probabilistic graph for community detection
  • 21. References [Akoglu et al 2015] Akoglu, Leman, Hanghang Tong, and Danai Koutra. "Graph based anomaly detection and description: a survey." Data Mining and Knowledge Discovery29.3 (2015): 626-688. [Ding et al 2012] Ding, Qi, et al. "Intrusion as (anti) social communication: characterization and detection." Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.