Bài giảng Computer Security - 9. Role-Based Access Control (RBAC) Role Classification Algorithm

3.3) Experiment Summary Accuracy of detection of malicious users by the classification algorithm ranges from 60% to 90% 90% of misbehaviors identified in a friendly environment Friendly environment - fewer than 20% of behaviors are malicious 60% of misbehaviors identified in an unfriendly environment Unfriendly environment - at least 90% of behaviors are malicious)

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9. Role-Based Access Control (RBAC) Role Classification AlgorithmProf. Bharat BhargavaCenter for Education and Research in Information Assurance and Security (CERIAS)andDepartment of Computer SciencesPurdue University bb@cs.purdue.eduCollaborators in the RAID Lab ( E. Terzi (former Graduate Student)Dr. Yuhui Zhong (former Ph.D. Student)Prof. Sanjay Madria (U. Missouri-Rolla) This research is supported by CERIAS and NSF grants from IIS and ANIR.RBAC Role Classification Algorithm - Outline1) Introduction2) Algorithm2.1) Algorithm Preliminaries2.2) Algorithm - Training Phase2.3) Algorithm - Classification Phase2.4) Classification Algorithm Pseudocode3) Experiments 3.1) Experiment 1: Classification Accuracy 3.2) Experiment 2: Detection and Diagnosis 3.3) Experiment Summary 1) IntroductionGoals for RBAC Role Classification Algorithm Detect intruders (malicious users) that enter the system Build user role profiles using a supervised clustering algorithmIncorporate the method in RBAC Server ArchitectureRBAC = Role Based Access ControlContextRole server architecture that dynamically assigns roles to users based on trust and credential informationRole classification algorithm phasesTraining phaseBuild clusters that correspond to the role profiles based on the previously selected training set of normal audit log recordsClassification phaseProcess on the run users audit records and specify whether they behave according to the profile of the role they are holding [E. Terzi, Y. Zhong, B. Bhargava et al., 2002]2) Algorithm 2.1) Algorithm PreliminariesData formatAudit log record [X1, X2 ,,Xn, Ri ]where: X1, X2 ,,Xn - n attributes of the audit log Ri : role held by user who created the log recordassumption:Every user can hold only one roleNo records of the form: [X1, X2 ,,Xn, Ri ] [X1, X2 ,,Xn, Rj]with Ri Rj2.2) Algorithm - Training PhaseTraining Phase – Building the ClusterCreate d dummy clusters, where d - nr of all discrete system rolesCentroid - the mean vector, containing the average values of the selected audit data attributes of all the users that belong to the specific rolea) For each training data record (Reccur ), calculate its Euclidean distance from each one of existing clusters b) Find the closest cluster Ccur to Reccur c) If role represented by Ccur= role of Reccur then cluster Reccur to Ccur else create a new cluster Cnew containing Reccur Cnew centroid: ReccurCnew role: Role of Reccur2.3) Algorithm - Classification PhaseClassification PhaseCalculate distance between the newly produced audit record Recnew of a user U and each existing clustera) Find cluster Cmin closer to Recnew b) Find cluster Ccur closest to Recnew c) if role represented by Ccur = role of Recnew then U is a normal user else U is an intruder and an alarm is raised Input: cluster list, audit log record Recfor every cluster Ci in cluster list calculate the distance between Rec and Cifind the closest cluster Cminif Cmin.role = Rec.role then return else raise alarmInput: Training audit log record [X1, X2 ,,Xn, R], where X1,,,Xn are attribute values, and R is the user’s roleOutput: A list of centroid representations of clusters [M1, M2 ,, Mn, pNum, R]Step 1: for every role Ri, create one cluster CiCi.role = Ri for every attribute Mk:2.4) Classification Algorithm PseudocodeStep 2: for every training record Reci calculateits Euclidean distance from existing clustersfind the closest cluster Cminif Cmin.role = Reci.rolethen reevaluate the attribute valueselse create new cluster Cj Cj.role = Reci.role for every attribute Mk: Cj.M k = Reci.Mk Training Phase – Build Clusters Classification Phase – Detect Malicious Users3) Experiments 3.1) Experiment 1: Classification AccuracyGoalTest classification accuracy of the methodDataTraining Set: 2000 recordsTest Set: Substi- tute 0% - 90% of records from the training set with new records Experiment results3.2) Experiment 2: Detection & DiagnosisGoalTest the ability of the algorithm to point out misbehaviors and specify the type of misbehaviorDataTraining Set: 2000 recordsTest Set: Modify the role attribute of 0%-90% of the 2000 records from the training set Experiment results3.3) Experiment SummaryAccuracy of detection of malicious users by the classification algorithm ranges from 60% to 90%90% of misbehaviors identified in a friendly environmentFriendly environment - fewer than 20% of behaviors are malicious60% of misbehaviors identified in an unfriendly environmentUnfriendly environment - at least 90% of behaviors are malicious)Our Research at PurdueWeb Site: http/www.cs.purdue.edu/homes/bbOver one million dollars in current support from: NSF, Cisco, Motorola, DARPASelected PublicationsB. Bhargava and Y. Zhong, "Authorization Based on Evidence and Trust", in Proc. of Data Warehouse and Knowledge Management Conference (DaWaK), Sept. 2002. E. Terzi, Y. Zhong, B. Bhargava, Pankaj, and S. Madria, "An Algorithm for Building User-Role Profiles in a Trust Environment", in Proc. of DaWaK, Sept. 2002 .A. Bhargava and M. Zoltowski, “Sensors and Wireless Communication for Medical Care,” in Proc. of 6th Intl. Workshop on Mobility in Databases and Distributed Systems (MDDS), Prague, Czechia, Sept. 2003.B. Bhargava, Y. Zhong, and Y. Lu, "Fraud Formalization and Detection", in Proc. of DaWaK, Prague, Czech Republic, Sept. 2003. THE END

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