! Towards a Human Factors Ontology for Cyber Security!

Transcription

1 ! Towards a Human Factors Ontology for Cyber Security! Alessandro Oltramari CyLab, Carnegie Mellon University Joint work with D. Henshel and M. Cains (IU), B. Hoffman (ARL)

2 Outline! A Holistic Perspective on Cyber Security Context Challenges Approach Framework Human Factors Ontology Infrastructure Features Examples 2

3 Context! 3

4 Challenges in Cyber Security! Complexity of the cyber environment Adversarial Attribution is difficult Indirect observations Data-intensive Human factors Cognitive biases & workload, personality, skills à decision making HCI Different information layers Simplistic risk and trust models Information fusion Incomplete/unavailable information Reliability Semantic transparency 4

5 Cyber Risk! Effective and successful efforts must consider impacts beyond the computer assets and network, taking a more holistic approach that considers the users, defenders, attackers, systems and infrastructures involved Assessing cyber security risk is a multicomponent, multi-tiered problem that involves hardware, software, environmental, and human factors. 5

6 Decisions and Risk! 6

7 Decisions and Trust! Environmental Workload T R U ST Social - Cogni1ve Behavioral Knowledge S it u a ti o n A w are n e ss D I M E N SI O N S 7

8 The Human Factors Framework! 8

9 My job? " "! Transform the conceptual framework into a machine readable ontology of trust-centric human factors Reusable semantic model Scalable Portable across platforms and tasks Actionable By automatic inference engines (DL-compliant) By artificial agents and probabilistic reasoning 9

10 Cyber Ontology Architecture (MITRE)! Obrst, Leo, Penny Chase, and Richard Markeloff. "Developing an Ontology of the Cyber Security Domain." STIDS Risk Trust 10

11 A Three-level Ontology for the Cyber CRA (CRATELO)! Foundational à object, event, quality, (DOLCE) Middle à risk, trust, vulnerability (SECCO) Specialized/Domain ontology of packet transmissions (PACO) ontology of SCADA vulnerabilities (TOSCA) 11

12 Aerial view of the result! 12

13 DOLCE s Quality Pattern! Quality is a specific attribute, e.g., reliability is a subattribute of trust w.r.t. a trustee (data). Quality-space is a abstract representation of an attribute s semantics, e.g., a binary space denoting the dichotomy reliable/unreliable - No topological commitment: the reliability space can be bi-dimensional or reflecting a probability distribution - Spatio-temporal dimensions can be added, e.g., when reliability is considered within a certain time frame (duration of transmission) in a specific network. Quality-region (part of a quality space) is a specific value associated to an attribute s semantics, e.g., binary value 1 standing for reliable, the discrete value 0.76, etc. 13

14 Example: Reliability of Network Delay! 14

15 7 Example: Risks of a Database Server! Problem: When applying the HUFO framework and model to a specific operation/scenario, what are the risks at each transition state and how is the overall risk affected? Which conceptual primitives are necessary and sufficient to include the model in order to describe risk? 15

16 State confidence variables tracked! S 0 Wai1ng State S 1 Connec1on Accepted Connec1on Determina1on: Secure / Insecure IDS determina-on = Secure IDS determina-on = InSecure S 1a Connec1on Broken S 2 Query Risk Variables: - Provenance - Where does connec1on come from? - Ports - User permission - Co- occurrence of network events - Database access System State Risk Modeled Tasks/ac1ons Transi1on between states

17 How to test this risk assessment approach dynamically?! Multi-Entity Bayesian Network! Mul1- En1ty Bayesian Networks (Laskey, 2008) integrate first order logic with Bayesian probability. 17

18 " OBElisk (Ontological-Bayesian Elicitation of Risk) 18

19 Variables as classes in the ontology Values are assigned to classes either as instances or as data types UnBBayes 19

20 Conclusions and Future work! Trust ontology framework embracing multiple human factors Comprehensive approach that considers foundational, middle, domain levels Simulation-friendly model Agent technologies (IBLOd à next talk!) Bayesian Networks Validation In diverse cyber scenarios At increasing level of complexity 20

21 Acknowledgements This research was sponsored by the Army Research Laboratory and was accomplished u n d e r C o o p e r a t i v e A g re e m e n t N u m b e r W911NF (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. 21

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