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Engineering a Distributed Intrusion Tolerant Database System Using COTS Components Peng Liu University of Maryland Baltimore County Feb 2001 1 The problem: Database Intrusion Tolerance • Attacks can succeed -> Intrusions •Intrusions can seriously impair data integrity and availability connect Authentication SQL Commands Access control Integrity control DBMS Database 2 Technical Objectives Engineering using COTS components a database system that can tolerate intrusions •Practical Database Intrusion Tolerance –Our approach: using COTS DBMS as main building blocks •Cost effective Database Intrusion Tolerance –Our approach: multi-layer defense, cost-effectiveness-performance analysis •Comprehensive Database Intrusion Tolerance –Our approach: transaction-level intrusion detection, isolation & masking, damage confinement, assessment, and repair •Adaptive Database Intrusion Tolerance –Our approach: self-stabilization by adaptation 3 Assumptions & Policies •What attacks are you considered? –All and only the attacks through malicious transactions •What assumptions are you making? –The proposed ITS facilities are trusted –The COTS DBMS executes transactions correctly •What policies can your project enforce? –The system will continuously execute transactions even in face of attacks –Damage caused by attacks will be automatically located and repaired –Located damage will be confined to not further spread –Suspicious users will be isolated or masked transparently –The degree of data integrity will be automatically stabilized –etc. 4 Existing Practice: Database Assurance •Authentication and access control cannot prevent all attacks •Integrity constraints are weak at prohibiting plausible but incorrect data •Concurrency control and recovery mechanisms cannot distinguish legitimate transactions from malicious ones •Automatic replication facilities and active database triggers can serve to spread the damage network 5 Expected major achievements •A cost-effective intrusion tolerant database system prototype •A family of innovative database intrusion tolerance techniques –Transaction-level intrusion detection –Intrusion isolation and masking –Multi-phase damage confinement –On the fly damage assessment and repair (implementation) –Adaptive database intrusion tolerance –Optimized load balance among ITS facilities •Identification and study of such ITS properties as adaptability, stability, and sensitivity 6 Our Approach 7 Transaction-Level vs. OS-Level Intrusion Tolerance Transaction-Level OS-Level •Good when attacks are via transactions •Good when attacks are via direct OS operations •Cannot handle OS-level attacks •Inefficient in handling malicious transactions Although both transaction-level and OS-level intrusion tolerance are necessary, we focus on transaction-level intrusion tolerance: –Most database attacks are (by insiders) through transactions –OS-level techniques can be easily integrated into our framework 8 Scheme 1: preliminary intrusion tolerance User SQL Commands Damage Confinement Mediator (Policy Enforcement) Repair SQL Commands Intrusion detector Proofs COTS DBMS Damage Repairer Proof collector Damage Assessor 9 Transaction-Level Intrusion Detection •Our goal: applying existing intrusion detection techniques to identifying malicious transactions •Key issues: –semantics-based intrusion detection –proof collection –using the detector as a protection tool –coupling OS-level and transaction-level intrusion detection SSN Start Date Salary 900000001 01/01/97 $58,000 900000001 01/01/98 $60,000 900000001 01/01/99 $62,000 900000001 01/01/00 $82,000 10 Application-Aware Intrusion Detection •Features: –application aware –portable –real time –protect the database from active bad transactions –integrate OS-level, table-level, sessionlevel, and transactionlevel semantics or statistics 11 Damage Assessment and Repair (Liu& Ammann & Jajodia 98,00) A history B1 G2 time The database G3 B1: read(x,z); write(x) G2: read(z); write(z) G3: read(x,y); write(y) x y z B1 Read-from G2 G3 A repair A dependency graph Undo B1 & G3 Our goal: implementation and evaluation 12 Current Status of Scheme 1 •A prototype of Scheme 1 is implemented except that –damage confinement is not implemented –a simulated intrusion detector is used, the real one is under coding •The prototype has around 20,000 lines of multi-threaded C++ code, running on top of a NT LAN and an Oracle server •The prototype proxies every SQL command, maintains the status of every session and every transaction, collects the proofs for every transaction, raises warnings, rolls back active bad transactions, locates the damage as a bad transaction is identified, and repairs the damage, all on-the-fly •Now the prototype is under testing and evaluation •We plan to demo this prototype on DISCEX II in June 13 A Limitation of Scheme 1 •The purpose of confinement is to prevent damage from spreading •The delay of damage assessment can cause ineffective confinement! User SQL Commands Damage Confinement Mediator (Policy Enforcement) B1’s write sets G2’s write sets Repair SQL Commands Intrusion detector B1 Proofs Proof collector B1 G4 Damage Repairer G2 Damage Assessor The database 14 Scheme 2: multi-phase confinement User SQL Commands Damage Confinement Phase 1 Later phases Mediator (Policy Enforcement) Repair SQL Commands Intrusion detector Proofs COTS DBMS Damage Repairer Proof collector Damage Assessor 15 Multi-Phase Confinement: An example To be confined: all data objects updated after time 5 except the data objects updated by G3 User SQL Commands Damage Confinement G3’s write set is clean Mediator (Policy Enforcement) B1 Repair SQL Commands Intrusion detector B1 Proofs Proof collector Damage Assessor B1[5] G4[15] Damage Repairer G2[7] G3[9] The database 16 Current Status of Scheme 2 17 A Limitation of Scheme 2 •For accuracy, intrusions can be detected with a significant delay •The delay can cause serious damage when an intrusion is detected •Quicker detection can decrease the amount of damage, but could mistake many legitimate transactions for malicious, and cause denial-of-service An user’s history Attack enforced t1 t2 Attack detected The database •Our goal: decreasing the amount of damage without losing detection accuracy and denial-of-service 18 Scheme 3: Isolation User SQL Commands Damage Confinement Suspicious trans. Mediator (Policy Enforcement) Intrusion detector Main database Isolating ... Isolating engine 1 engine n Damage Repairer read Damage Assessor merge 19 Current Status of Scheme 3 •Our preparation •Our current focus: design and implementation (is challenging!) 20 A Limitation of Scheme 3 •To reduce cost, very few users (i.e., one) can be isolated within a single engine •However, to avoid causing damage on the main database, the number of suspicious transactions can be large •Hence, isolating every suspicious transaction can be too expensive! •Our solution •Treating very suspicious and less suspicious users differently •Isolating very suspicious users •Masking less suspicious users •Advantage: better cost-effectiveness 21 Scheme 4: Masking User SQL Commands Damage Confinement Mediator (Policy Enforcement) Less suspicious trans. Very suspicious trans. Intrusion detector Damage Assessor Damage Repairer Masking engine 1 Main DB Isolating engine 1 ... Isolating engine n ... Masking engine n read merge 22 Intrusion Masking: An Example Three less suspicious users: Ui : Ti1 Uj : T j 1 Uk : T k 1 Main history Masking history 1 Masking history 2 Advantages: •Quicker recovery •Less cost clean T[i1] T[k1] T[j1] •If T[i1], T[j1], and T[k1] are all malicious, the main database is valid •If T[i1] and T[j1] are malicious, but T[k1] is not, then masking engine 2 is valid •If T[i1] and T[k1] are malicious, but T[j1] is not, then though none is valid, reexecuting T[j1] on the main history can produce the valid database 23 A Limitation of Scheme 4 •Scheme 4 is not adaptive by nature •Adaptation can give better resilience and cost-effectiveness •There is no automatic way for the system to adaptively adjust its defense behavior according to: •the characteristics of recent and ongoing attacks •its current performance against these attacks •Although the SSO can dynamically reconfigure some of its components, manual reconfiguration operations are ad-hoc, not scalable, and prone to errors •Our goal: adaptive database intrusion tolerance 24 Scheme 5: Self-Stabilization •Self-Stabilization: the degree of data integrity should be able to be automatically stabilized within a tolerable range no matter how the system is attacked User SQL Commands Damage Confinement Mediator (Policy Enforcement) Intrusion detector Damage Assessor Damage Repairer Tolerable range State variable feedback The controller Main database Isolation engines Masking engines 25 The database Optimized Load Balance •Observation: •Different load configurations usually cause different cost-effectiveness •A load configuration can cause very different cost-effectiveness in different situations •An example of load configuration: •the percentage of isolated users •the percentage of masked users •the percentage of malicious users •the number of masking engines used •the average interval of state variable feedback •... •Our goal: adaptive load configuration optimization •Mechanism: the controller can be responsible for this job 26 Metrics to measure success (better cost-effectiveness) •Cost –time, space needed for tolerating intrusions •Effectiveness –how many intrusions are detected, isolated, or masked –how many mistakes are made –how effectively can the damage be confined –how quick can the damage be assessed and repaired –how well can the system be adapted –availability: how often is a legitimate request rejected –integrity: how well can data integrity be preserved under attacks •Performance –system throughput –response time 27 Task Schedule Schedule FY01 FY02 Intrusion Detection Assessment & Repair Confinement Isolation and Masking Self-Stabilization Design Separate Demonstrations Integrated Demonstrations 28 Technology Transfer •Technical papers published in leading technical meetings and technical reports • Release and dissemination of the prototype in source and binary forms •Pursuing technology transition through major commercial DBMS vendors. The technologies can either be absorbed into their DBMS kernels, or be commercialized as intrusion tolerance wrappers •Starting a company to commercialize the technologies and provide flexible services to arm customers' database systems with necessary intrusion tolerance facilities 29 Questions? Thank you! 30 Multi-layer representation of our approach 31