Online Program Home
  My Program

Abstract Details

Activity Number: 630 - Machine Learning Applications
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323572
Title: Cyber-Security: a Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process
Author(s): Nawa Raj Pokhrel*
Companies:
Keywords: Vulnerability ; Attack Graph ; Markov Model ; CVSS ; NVD ; IDS
Abstract:

There are several security metrics developed to protect the computer networks. In general, common security metrics focus on qualitative and subjective aspect of network lacking formal statistical models. In the present study, we propose a stochastic model to quantify the risk associated with the overall network using Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) framework. The model we developed uses host access graph to represent the network environment. Utilizing the developed model, one can filter the large amount of information available by making a priority list of vulnerable nodes present in the network. Once a priority list is prepared, network administrators can make software patch decisions. Gaining in depth understanding of the risk and priority level of each host helps individuals to make the decisions like deployment of security products and to design network typologies.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association