Peng Liu, Jun Dai, Xiaoyan Sun, Robert Cole Penn State University pliu@ist.psu

1. Multi-Step Attack Defense Operating Point Estimation via Bayesian Modeling under Parameter Uncertainty Peng Liu, Jun Dai, Xiaoyan Sun, Robert Cole Penn State University pliu@ist.psu.edu ARO Cyber Situation Awareness MURI

2. Cognitive Models & Decision Aids • Instance Based Learning Models • Simulation • Measures of SA & Shared SA • Software • Sensors, probes • Hyper Sentry • Cruiser • Information Aggregation & Fusion • Transaction Graph methods • Damage assessment • Automated • Reasoning • Tools • R-CAST • Plan-based narratives • Graphical models • Uncertainty analysis Data Conditioning Association & Correlation Multi-Sensory Human Computer Interaction Computer network • Enterprise Model • Activity Logs • IDS reports • Vulnerabilities Real World Computer network System Analysts Test-bed ARO Cyber Situation Awareness MURI

3. System Architecture – Cyber Security Perspective ARO Cyber Situation Awareness MURI

4. Year 4 projects Multi-Step Attack Defense Operating Point Estimation via Bayesian Modeling -- PhD Dissertation Snake: Discover and Profile Network Service Dependencies via network wide SCDGs -- Tool & paper (in progress) Patrol: Zero-day attack path detection via network-wide SCDGs -- ESORICS’13 -- Tool Cross-layer Bayesian networks to manage uncertainty in cyber SA -- Paper (in progress) CLR: Automated recovery plan generation -- ICICS’13 ARO Cyber Situation Awareness MURI

5. Year 4 accomplishments Publications: -- 1 PhD dissertation -- 5 journal papers -- 11 conference papers -- 1 book chapter Tools: -- Patrol -- Snake (in progress) Tech transfer: DoD SBIR 12.3 Phase I OSD12-IA5 project “An Integrated Threat feed Aggregation, Analysis, and Visualization (TAAV) Tool for Cyber Situational Awareness,” funded, led by Intelligent Automation, Inc. Students: -- Jun Dai (50%), PhD -- Xiaoyan Sun (50%), PhD -- Robert Cole (0%), PhD ARO Cyber Situation Awareness MURI

6. Research Highlight: Multi-step attack defense operating point estimation via Bayesian modeling ARO Cyber Situation Awareness MURI

7. Motivation • No real world IDS system is perfect. • -- When an IDS system is configured to achieve a higher true positive rate, usually it would suffer from a higher false positive rate • Such a (true positive rate, false positive rate) tradeoff is called an operating pointof the IDS. • The cyber operator can keep tuning the IDS until the estimated operating point is close enough to the desired operating point. ARO Cyber Situation Awareness MURI

8. Problem Statement • Due to the inherent uncertainty associated with gaining cyber SA, operating point estimation won’t be 100% accurate. • Although the estimation problem for individual exploits has been studied in the literature, the estimation problem for multi-step attacks (a chain of exploits) under model parameter uncertainty has not yet been studied. • -- Traditional IDS systems do not explicitly consider uncertainty ARO Cyber Situation Awareness MURI

9. Innovation Claim We developed the first quantitative multi-step intrusion detection system operating point estimation framework based on Bayesian modeling. ARO Cyber Situation Awareness MURI

10. Approach • Do generalized alert correlation analysis. • Instead of requiring (certain types of) attribute value match (e.g., the destination IP address of one alert matches the source IP of another)between two IDS alerts, we model the rationale for such matches using conditional probabilities and a Bayesian net. • --Similar modeling is used in the ACSAC’04 work by Ning group for a different purpose. ARO Cyber Situation Awareness MURI

11. Research Contribution 1 • We developed a novel Bayesian operating point estimation model: • -- General multi-step attack strategies can be precisely specified as a “query” against the model which corresponds to a specific Bayesian network. • -- Our model can propagate parameter uncertainty through the model to a query result. ARO Cyber Situation Awareness MURI

12. Research Contribution 2 Shift from per-exploit detection to per-chain: In the case of zero parameter uncertainty, we developed an efficient algorithm to enumerate useful operating points within the 2-dimensional design space of: [detection rate vs. false positive rate] ARO Cyber Situation Awareness MURI

13. Research Contribution 3 For the uncertain parameter case, we studied the special case of serial order multi-step attacks. We theoretically proved that there exist specific cases under which model parameter uncertainty won’t produce output uncertainty. ARO Cyber Situation Awareness MURI

14. Research Contribution 4 • We found that operating points could become 2-dimensional operating boxes. • The general problem of operating box enumeration is highly computationally complex. We conducted experiments evaluating two heuristic solutions. • Experimental results show a heuristic solution (our operating point enumeration algorithm) provides results very close to full enumeration. • Results show the significance of uncertainty in the multi-step attack detection cases considered. ARO Cyber Situation Awareness MURI

15. Year 5 Joint project with NIST: Cloud-wide vulnerability analysis -- In progress Snake: Discover and Profile Network Service Dependencies via network wide SCDGs -- Tool & paper (in progress) Joint project with NEC Labs: System-call-level security intelligence -- In progress Cross-layer Bayesian networks to manage uncertainty in cyber SA -- In progress Tool integration: with GMU, NCSU, etc. -- In progress ARO Cyber Situation Awareness MURI

16. ARO MURI: Computer-aided Human-Centric Cyber Situation Awareness: SKRM Inspired Cyber SA Analytics Penn State University (Peng Liu) Tel. 814-863-0641, E-Mail: pliu@ist.psu.edu • Objectives: Improve Cyber SA through: • A Situation Knowledge Reference Model (SKRM) • A systematic framework for uncertainty management • Cross-knowledge-abstraction-layer SA analytics • Game theoretic SA analytics • DoD Benefit: • Innovative SA analytics lead to improved capabilities in gaining cyber SA. Uncertainty analysis • Accomplishments • A suite of SKRM inspired SA analytics • A Bayesian Networks approach to uncertainty • A method to identify zero-day attack paths • A signaling game approach to analyze cyber • attack-defense dynamics • Challenges • Systematic evaluation & validation • Scientific/Technical Approach • Leverage knowledge of “us” • Cross-abstraction-layer situation knowledge • integration • Network-wide system all dependency analysis • Probabilistic graphic models • Game theoretic analysis ARO Cyber Situation Awareness MURI

17. Q & A Thank you. ARO Cyber Situation Awareness MURI