DOWeR Detecting Outliers in Web Service Requests Master’s Presentation of Christian Blass

Overview 1.Motivation Web Services (SOAP and REST) Vulnerabilities Existing Mitigation Methods 2.Key Ideas of DOWeR Detection Proxy Outlier Detection N-Gram Tries as Feature Vectors 3.Testing Environment ROC vs. Precision and Recall Architecture of Evaluation Engine 4.Tested Properties and Potential Issues 5.Outlook and Conclusion 6.Demonstration of DOWeR

1. Motivation Rising Popularity of Web Services  Amazon, eBay, Google Hard to Develop Secure  Developers don’t have knowledge about security issues  Vulnerabilities on the application logic Aims of Attackers  Overloading service  Theft of private data  Compromising client machines Aims of Research  Securing existing Web Services  No insights to Web Service structure  No alteration of code  No deep understanding of security

Web Services and Protocols for Implementation Definition of Web Services  “A Web Service is a software system designed to support interoperable machine-to-machine interaction over a network.” (W3C) Protocols  SOAP XML based Envelope, header, body Attributes embedded to SOAP body  REST Based on HTTP commands Resources GET, POST, DELETE, … Search Securtiy

Vulnerabilities and Countermeasures Vulnerabilities  SOAP Vulnerabilities Exploiting XML parsers DTD Entity Reference Attack  SQL injection Malicious alteration of SQL query SELECT * FROM data WHERE user = ‘ ’ SELECT * FROM data WHERE user = ‘A’ or user = ‘B’  Cross Site Scripting Attacker forces execution of script on third party client Countermeasures  SOAP Standards like WS Security, WS Policy, and XML Schema  Intrusion detection

NetShield - Extension of Mitigator NetShield Mitigator  Monitors traffic (packet types) on network layer  Generates firewall rules  Can only react to attacks already in progress Extension  Network layer vs. application layer Attacks hide in payload Traffic on network layer normal  Proxy

2. Key Ideas of DOWeR Architecture Classifier Client Attacker Web Service DOWeR Classifier

Knn Outlier Detection Unlabeled (real) data ? Does a new document fit in the monitored pattern k-nearest (k=3) Compare to threshold computed from other documents Feature A Feature B

Language Models as Features Payload of REST request  Byte distribution (PAYL, NIDES)  Properties of certain Attributes  N-Grams Frequency Difference between 3-gram Frequencies of an IIS unicode attack and normal HTTP traffic

Storing N-grams in Trie Data Structure b a r n c a r d b a n d c a r d 5 k

Distance Between Two Tries b a r n c a b a n d c a r d 5 k m - (x,y) |4|+|8|

3. Testing Environment: Receiver Operating Characteristic (ROC) Score Threshold

ROC vs. Precision and Recall ROC and Area Under Curve (AUC)  Use all possible thresholds  Estimate the capability of a method of separating data  Good to compare methods with each other Precision and Recall  A certain threshold needs to be fixed  Estimate accuracy of classifier

Architecture of Testing Environment Traffic Generator Evaluation Corpus Training Corpus Label Checker Classifier Instance Label Training Attack Free Instances Mixed Instances Quality Measures Evaluation ROC, AUC, Precision, Recall

4. Tested Properties and Potential Issues

Tested Properties and Potential Issues Threshold estimation  Hard to find good thresholds as they depend on The used domain Configurations made  Thresholds might change over time as Instances in model change Distributions of requests might change

Tested Properties and Potential Issues Impact of Free Text Robustness to Changes Short terms with fixed syntaxLong terms with no constraints lower score higher score Monitored Instances Service A Service B New Service

Tested Properties and Potential Issues a) b)c) Possibility of slow alteration of model Intermediate instances close to normal instances and attack to be launched Example using k = 1 1.Red malicious instance further away from nearest neighbor as allowed by threshold 2.Orange specially crafted instance added to model 3.Orange instance becomes nearest neighbor of attack and the score of the attack drops below the threshold resulting in a successful attack

Tested Properties and Potential Issues Runtime  Purely memory based approach  New instances need to be matched against all instances in model  Critical as overloading the classifier might also result in DoS Model Size n=1n=2n=3n= Runtime in ms

5. Outlook and Conclusion Aim: Securing existing Web Services in-depth knowledge In general method works well  90% accuracy with no false positives  Manual tuning of attributes Possible improvements  Threshold estimation Adjustment to changes in domain Adaptive threshold  Outlier score and algorithm  Runtime Pre-clustering of model

6. Demo