ARO Cyber Situation Awareness MURI

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

System Analysts

Computer network

SoftwareSensors, probes• Hyper Sentry• Cruiser

Mu

lti-

Sen

sory

Hu

man

C

om

pu

ter

Inte

ract

ion

• Enterprise Model• Activity Logs • IDS reports

• Vulnerabilities

Cognitive Models & Decision Aids• Instance Based Learning Models

• Simulation• Measures of SA & Shared SA

• • •

Da

ta C

on

dit

ion

ing

As

so

cia

tio

n &

Co

rre

lati

on

Automated Reasoning Tools• R-CAST• Plan-based

narratives• Graphical

models• Uncertainty

analysis

Information Aggregation

& Fusion• Transaction Graph methods

•Damage assessment

Computer network

• •

•

Real World

Test-bed

ARO Cyber Situation Awareness MURI

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

Patrol: Zero-day attack path detection via network-wide SCDGs-- ESORICS’13-- Tool

Snake: Discover and Profile Network Service Dependencies via network wide SCDGs-- Tool & paper (in progress)

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

Multi-step attack defense operating point estimation via Bayesian modeling

Research Highlight:

ARO Cyber Situation Awareness MURI

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 point of 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

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

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

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

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

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

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

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.

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Year 5

Snake: Discover and Profile Network Service Dependencies via network wide SCDGs-- Tool & paper (in progress)

Cross-layer Bayesian networks to manage uncertainty in cyber SA-- In progress

Joint project with NIST: Cloud-wide vulnerability analysis-- In progress

Joint project with NEC Labs: System-call-level security intelligence -- In progress

Tool integration: with GMU, NCSU, etc. -- In progress

ARO Cyber Situation Awareness MURI

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.

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

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

Uncertainty analysis

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Q & A

Thank you.