Monday, 3 December 2012
13:30 - 17:00

Sussex

At present, computer networks constitute the core component of information technology infrastructures in areas such as power grids, financial data systems and emergency communication systems. Protection of these networks from malicious intrusions is critical to the economy and security of our nation. Protection of enterprise networks from malicious intrusions is critical to the economy and security of our nation. To improve the security of these information systems, it is necessary to measure the amount of security provided by different networks configurations. A standard model for security analysis will enable us to answer questions such as "are we more secure than yesterday" or "how does the security of one network configuration compare with another one". Also, having a standard model to measure network security will bring together users, vendors and researchers to evaluate methodologies and products for network security.

The objective of this tutorial is to give an overview of the techniques and challenges for security metrics and risk analysis of enterprise networks. In this tutorial, we will present some basics of security metrics and a methodology for security risk analysis that is based on the model of attack graphs and the Common Vulnerability Scoring System (CVSS).

The area of how to measure security for enterprise systems is a challenging one. In security, business leaders ask the following questions:

• How effective are my security processes
• Is my security posture better off than what it was this time last year
• How do I compare with my peers
• Am I spending the right amount of money on security
• How can I reduce my risk

These are difficult questions to answer. Security metrics can help an organization for

• Understanding security risks
• Understand weaknesses in security infrastructures
• Measure performance of counter measure processes
• Recommend process improvements for security

In this tutorial, we will first present basic concepts about security metrics and then present techniques for security risk analysis of enterprise networks using Attack Graphs.

An essential type of security risk analysis is to determine the level of compromise possible for important hosts in a network from a given starting location. This is a complex task as it depends on the network topology, security policy in the network as determined by the placement of firewalls, routers and switches and on vulnerabilities in hosts and communication protocols. Traditionally, this type of analysis is performed by a red team of computer security professionals who actively test the network by running exploits that compromise the system. Red team exercises are effective, however they are labor intensive and time consuming. There is a need for alternate approaches that can work with host vulnerability scans.

In this tutorial, we will present a methodology for security risk analysis that is based on the model of attack graphs and the Common Vulnerability Scoring System (CVSS). Attack graphs illustrate the cumulative effect of attack steps, showing how individual steps can potentially enable an attacker to gain privileges deep within the network. CVSS is a risk measurement system that gives the likelihood that a single attack step is successfully executed. In this tutorial we present a methodology to measure the overall system risk by combining the attack graph structure with CVSS. Our technique analyzes all attack paths through a network, providing a probabilistic metric of the overall system risk.

Prerequisites. None. The course is intended to be useful to IT Security Professionals in industry and academia, researchers in computer and network security, and graduate students.

Outline:

1. Basics of Enterprise Security Metrics
2. Examples of Technical Security Metrics
3. Automating Metrics Calculations Using a Data Model
4. Common Vulnerability Scoring System (CVSS)
5. Attack Graphs, Bayesian Networks and MulVAL Tool for generating Attack Graphs
6. Security Risk Analysis of Enterprise Systems using Attack Graphs
7. Challenges and Future Directions
8. Conclusions