Monday, 26 October 2015

Cyber security risk of nuclear facilities using Bayesian networks

Scientists from Korea (Jinsoo Shin, Hanseong Son, Rahman Khalilur, and Gyunyoung Heo) have published an article describing their Bayesian network model for assessing cyber security risk of nuclear facilities (using the AgenaRisk tool). It is based on combining two models - one which is process based (considers how well security procedures were followed) and the other which is considers the system architecture (considering vulnerabilities and controls). The full paper is here:

Shin, J., Son, H., Khalil ur, R., & Heo, G. (2015). Development of a cyber security risk model using Bayesian networks. Reliability Engineering & System Safety, 134, 208–217. doi:10.1016/j.ress.2014.10.006

Bayesian Networks for Risk Assessment of Public Safety and Security Mobile Service

A new paper by Matti Peltola and Pekka Kekolahti of the Aalto University (School of Electrical Engineering) in Finland uses Bayesian Networks and the AgenaRisk tool to gain a deeper understanding of the availability of Public Safety and Security (PSS) mobile networks and their service under different conditions. The paper abstract states:
A deeper understanding of the availability of Public Safety and Security (PSS) mobile networks and their service under different conditions offers decision makers guidelines on the level of investments required and the directions to take in order to decrease the risks identified. In the study, a risk assessment model for the existing PSS mobile service is implemented for both a dedicated TETRA PSS mobile network as well as for a commercial 2G/3G mobile network operating under the current risk conditions. The probabilistic risk assessment is carried out by constructing a Bayesian Network. According to the analysis, the availability of the dedicated Finnish PSS mobile service is 99.1%. Based on the risk assessment and sensitivity analysis conducted, the most effective elements for decreasing availability risks would be duplication of the transmission links, backup of the power supply and real-time mobile traffic monitoring. With the adjustment of these key control variables, the service availability can be improved up to the level of 99.9%. The investments needed to improve the availability of the PSS mobile service from 99.1 % to 99.9% are profitable only in highly populated areas. The calculated availability of the PSS mobile service based on a purely commercial network is 98.8%. The adoption of a Bayesian Network as a risk assessment method is demonstrated to be a useful way of documenting different expert knowledge as a common belief about the risks, their magnitudes and their effects upon a Finnish PSS mobile service.
Full reference details:
Peltola, M. J., & Kekolahti, P. (2015). Risk Assessment of Public Safety and Security Mobile Service. In 2015 10th International Conference on Availability, Reliability and Security (pp. 351–359). IEEE. doi:10.1109/ARES.2015.65

Sunday, 18 October 2015

What is the value of missing information when assessing decisions that involve actions for intervention?

This is a summary of the following new paper:

Constantinou AC, Yet B, Fenton N, Neil M, Marsh W  "Value of Information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences". Artif Intell Med. 2015 Sep 8  doi:10.1016/j.artmed.2015.09.002. The full pre-publication version can be found here.

Most decision support models in the medical domain provide a prediction about a single key unknown variable, such as whether a patient exhibiting certain symptoms is likely to have (or develop) a particular disease.

However we seek to enhance decision analysis by determining whether a decision based on such a prediction could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to the decision. In particular we wish to incorporate interventional actions and counterfactual analysis, where:
  • An interventional action is one that can be performed to manipulate the effect of some desirable future outcome. In medical decision analysis, an intervention is typically represented by some treatment, which can affect a patient’s health outcome.
  • Counterfactual analysis enables decision makers to compare the observed results in the real world to those of a hypothetical world; what actually happened and what would have happened under some different scenario.
The method we use is based on the underlying principle of Value of Information. This is a technique initially proposed in economics for the purposes of determining the amount a decision maker would be willing to pay for further information that is currently unknown within the model.

The type of predictive decision support models to which our work applies are Bayesian networks. These are graphical models which represent the causal or influential relationships between a set of variables and which provide probabilities for each unknown variable.

The method is applied to two real-world Bayesian network models that were previously developed for decision support in forensic medical sciences. In these models a decision maker (such as a probation officer or a clinician) has to determine whether to release a prisoner/patient based on the probability of the (unknown) hypothesis variable: “individual violently reoffends after release”. Prior to deciding on release, the decision maker has the option to simulate various interventions to determine whether an individual’s risk of violence can be managed to acceptable levels. Additionally, the decision maker may have the option to gather further information about the individual. It is possible that knowing one or more of these unobserved factors may lead to a different decision about release.

We used the method to examine the average information gain; that is, what we learn about the importance of the factors that remain unknown within the model. Based on six different sets of experiments with various assumptions we show that:
  1. the average relative percentage gain in terms of Value of Information ranged between 11.45% and 59.91% (where a gain of X% indicates an expected X% relative reduction of the risk of violent reoffence);
  1. the potential amendments in Decision Making, as a result of the expected information gain, ranged from 0% to 86.8% (where an amendment of X% indicates that X% of the initial decisions are expected to have been altered).
The key concept of the method is that if we had known that the individual was, for example, a substance misuser, we would have arranged for a suitable treatment; whereas without having information about substance misuse it is impossible to arrange such a treatment and, thus, we risk not treating the individual in the case where he or she is a substance misuser.

The method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from seeking information about that particular set of risk factors.

This summary can also be found on the Atlas of Science

Thursday, 15 October 2015

Talk: Bayesian networks: why smart data is better than big data

by Prof. Norman Fenton from the School of Electronic Engineering and Computer Science (QMUL)
WHEN: Fri, 16th October 2 - 3 pm
WHERE: People's Palace PP2 (Mile End Campus)

"This talk will provide an introduction to Bayesian networks which, due to relatively recent algorithmic breakthroughs, has become an increasingly popular technique for risk assessment and decision analysis. I will provide an overview of successful applications (including transport safety, medical, law/forensics, operational risk, and football prediction). What is common to all of these applications is that the Bayesian network models are built using a combination of expert judgment and (often very limited) data. I will explain why Bayesian networks ‘learnt’ purely from data – even when ‘big data’ is available - generally do not work well."

All are welcome. The seminar consists of an app. 45 min long lecture and discussion.
In case of any questions, feel free to contact me.
Hope to see you tomorrow,

Judit Petervari
Judit Petervari
PhD Student

Biological and Experimental Psychology Group
School of Biological and Chemical Sciences
Queen Mary University of London
Mile End Road
E1 4NS London
United Kingdom

Office: G.E. Fogg Building, Room 2.16